@article{abdia2017,
  title = {Propensity Scores Based Methods for Estimating Average Treatment Effect and Average Treatment Effect among Treated: {{A}} Comparative Study},
  author = {Abdia, Y. and Kulasekera, K. B. and Datta, S. and Boakye, M. and Kong, M.},
  year = {2017},
  month = sep,
  journal = {Biom J},
  volume = {59},
  number = {5},
  pages = {967--985},
  issn = {1521-4036 (Electronic) 0323-3847 (Linking)},
  doi = {10.1002/bimj.201600094},
  abstract = {Propensity score based statistical methods, such as matching, regression, stratification, inverse probability weighting (IPW), and doubly robust (DR) estimating equations, have become popular in estimating average treatment effect (ATE) and average treatment effect among treated (ATT) in observational studies. Propensity score is the conditional probability receiving a treatment assignment with given covariates, and propensity score is usually estimated by logistic regression. However, a misspecification of the propensity score model may result in biased estimates for ATT and ATE. As an alternative, the generalized boosting method (GBM) has been proposed to estimate the propensity score. GBM uses regression trees as weak predictors and captures nonlinear and interactive effects of the covariate. For GBM-based propensity score, only IPW methods have been investigated in the literature. In this article, we provide a comparative study of the commonly used propensity score based methods for estimating ATT and ATE, and examine their performances when propensity score is estimated by logistic regression and GBM, respectively. Extensive simulation results indicate that the estimators for ATE and ATT may vary greatly due to different methods. We concluded that (i) regression may not be suitable for estimating ATE and ATT regardless of the estimation method of propensity score; (ii) IPW and stratification usually provide reliable estimates of ATT when propensity score model is correctly specified; (iii) the estimators of ATE based on stratification, IPW, and DR are close to the underlying true value of ATE when propensity score is correctly specified by logistic regression or estimated using GBM.},
  keywords = {*Models; Statistical,Biometry/*methods,Computer Simulation,Logistic Models,Propensity Score}
}

@article{adossa2021,
  title = {Computational Strategies for Single-Cell Multi-Omics Integration},
  author = {Adossa, N. and Khan, S. and Rytkonen, K. T. and Elo, L. L.},
  year = {2021},
  journal = {Comput Struct Biotechnol J},
  volume = {19},
  pages = {2588--2596},
  issn = {2001-0370 (Print) 2001-0370 (Linking)},
  doi = {10.1016/j.csbj.2021.04.060},
  abstract = {Single-cell omics technologies are currently solving biological and medical problems that earlier have remained elusive, such as discovery of new cell types, cellular differentiation trajectories and communication networks across cells and tissues. Current advances especially in single-cell multi-omics hold high potential for breakthroughs by integration of multiple different omics layers. To pair with the recent biotechnological developments, many computational approaches to process and analyze single-cell multi-omics data have been proposed. In this review, we first introduce recent developments in single-cell multi-omics in general and then focus on the available data integration strategies. The integration approaches are divided into three categories: early, intermediate, and late data integration. For each category, we describe the underlying conceptual principles and main characteristics, as well as provide examples of currently available tools and how they have been applied to analyze single-cell multi-omics data. Finally, we explore the challenges and prospective future directions of single-cell multi-omics data integration, including examples of adopting multi-view analysis approaches used in other disciplines to single-cell multi-omics.},
  pmcid = {PMC8114078},
  keywords = {Clustering,Integration,Multi-omics,personal relationships that could have appeared to influence the work reported in,Single-cell,this paper.}
}

@article{argelaguet2018,
  title = {Multi-{{Omics Factor Analysis-a}} Framework for Unsupervised Integration of Multi-Omics Data Sets},
  author = {Argelaguet, R. and Velten, B. and Arnol, D. and Dietrich, S. and Zenz, T. and Marioni, J. C. and Buettner, F. and Huber, W. and Stegle, O.},
  year = {2018},
  month = jun,
  journal = {Mol Syst Biol},
  volume = {14},
  number = {6},
  pages = {e8124},
  issn = {1744-4292 (Electronic) 1744-4292 (Linking)},
  doi = {10.15252/msb.20178124},
  abstract = {Multi-omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets are lacking. We present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy-chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single-cell multi-omics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation.},
  pmcid = {PMC6010767},
  keywords = {*data integration,*Datasets as Topic,*dimensionality reduction,*multi-omics,*personalized medicine,*single-cell omics,Antineoplastic Agents/therapeutic use,Computational Biology/*methods,Computer Simulation,Humans,Leukemia; Lymphocytic; Chronic; B-Cell/drug therapy/genetics,Models; Statistical,Oxidative Stress,Software,Transcriptome}
}

@article{argelaguet2020,
  title = {{{MOFA}}+: A Statistical Framework for Comprehensive Integration of Multi-Modal Single-Cell Data},
  author = {Argelaguet, R. and Arnol, D. and Bredikhin, D. and Deloro, Y. and Velten, B. and Marioni, J. C. and Stegle, O.},
  year = {2020},
  month = may,
  journal = {Genome Biol},
  volume = {21},
  number = {1},
  pages = {111},
  issn = {1474-760X (Electronic) 1474-7596 (Linking)},
  doi = {10.1186/s13059-020-02015-1},
  abstract = {Technological advances have enabled the profiling of multiple molecular layers at single-cell resolution, assaying cells from multiple samples or conditions. Consequently, there is a growing need for computational strategies to analyze data from complex experimental designs that include multiple data modalities and multiple groups of samples. We present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of single-cell multi-modal data. MOFA+ reconstructs a low-dimensional representation of the data using computationally efficient variational inference and supports flexible sparsity constraints, allowing to jointly model variation across multiple sample groups and data modalities.},
  pmcid = {PMC7212577},
  keywords = {*Data integration,*Factor analysis,*Factor Analysis; Statistical,*Multi-omics,*Single cell,*Single-Cell Analysis,Animals,DNA Methylation,Embryonic Development,Frontal Lobe/metabolism,Mice,Sequence Analysis; RNA}
}

@article{argirion2017,
  title = {Serum {{Insulin}}, {{Glucose}}, {{Indices}} of {{Insulin Resistance}}, and {{Risk}} of {{Lung Cancer}}},
  author = {Argirion, Ilona and Weinstein, Stephanie J. and M{\"a}nnist{\"o}, Satu and Albanes, Demetrius and Mondul, Alison M.},
  year = {2017},
  month = oct,
  journal = {Cancer Epidemiology, Biomarkers \& Prevention: A Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology},
  volume = {26},
  number = {10},
  pages = {1519--1524},
  issn = {1538-7755},
  doi = {10.1158/1055-9965.EPI-17-0293},
  abstract = {Background: Although insulin may increase the risk of some cancers, few studies have examined fasting serum insulin and lung cancer risk.Methods: We examined serum insulin, glucose, and indices of insulin resistance [insulin:glucose molar ratio and homeostasis model assessment of insulin resistance (HOMA-IR)] and lung cancer risk using a case-cohort study within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study of Finnish men. A total of 196 cases and 395 subcohort members were included. Insulin and glucose were measured in fasting serum collected 5 to 12 years before diagnosis. Cox proportional hazards models were utilized to estimate the relative risk of lung cancer.Results: The average time between blood collection and lung cancer was 9.6 years. Fasting serum insulin levels were 8.7\% higher in subcohort members than cases. After multivariable adjustment, men in the fourth quartile of insulin had a significantly higher risk of lung cancer than those in the first quartile [HR = 2.10; 95\% confidence interval (CI), 1.12-3.94]. A similar relationship was seen with HOMA-IR (HR = 1.83; 95\% CI, 0.99-3.38). Risk was not strongly associated with glucose or the insulin:glucose molar ratio (Ptrend = 0.55 and Ptrend = 0.27, respectively).Conclusions: Higher fasting serum insulin concentrations, as well as the presence of insulin resistance, appear to be associated with an elevated risk of lung cancer development.Impact: Although insulin is hypothesized to increase risk of some cancers, insulin and lung cancer remain understudied. Higher insulin levels and insulin resistance were associated with increased lung cancer risk. Although smoking cessation is the best method of lung cancer prevention, other lifestyle changes that affect insulin concentrations and sensitivity may reduce lung cancer risk. Cancer Epidemiol Biomarkers Prev; 26(10); 1519-24. \textcopyright 2017 AACR.},
  langid = {english},
  pmcid = {PMC5626607},
  pmid = {28698186},
  keywords = {Double-Blind Method,Female,Humans,Insulin,Insulin Resistance,Lung Neoplasms,Male,Middle Aged,Risk Factors}
}

@article{bastien2015,
  title = {Deviance Residuals-Based Sparse {{PLS}} and Sparse Kernel {{PLS}} Regression for Censored Data},
  author = {Bastien, P. and Bertrand, F. and Meyer, N. and {Maumy-Bertrand}, M.},
  year = {2015},
  month = feb,
  journal = {Bioinformatics},
  volume = {31},
  number = {3},
  pages = {397--404},
  issn = {1367-4811 (Electronic) 1367-4803 (Linking)},
  doi = {10.1093/bioinformatics/btu660},
  abstract = {MOTIVATION: A vast literature from the past decade is devoted to relating gene profiles and subject survival or time to cancer recurrence. Biomarker discovery from high-dimensional data, such as transcriptomic or single nucleotide polymorphism profiles, is a major challenge in the search for more precise diagnoses. The proportional hazard regression model suggested by Cox (1972), to study the relationship between the time to event and a set of covariates in the presence of censoring is the most commonly used model for the analysis of survival data. However, like multivariate regression, it supposes that more observations than variables, complete data, and not strongly correlated variables are available. In practice, when dealing with high-dimensional data, these constraints are crippling. Collinearity gives rise to issues of over-fitting and model misidentification. Variable selection can improve the estimation accuracy by effectively identifying the subset of relevant predictors and enhance the model interpretability with parsimonious representation. To deal with both collinearity and variable selection issues, many methods based on least absolute shrinkage and selection operator penalized Cox proportional hazards have been proposed since the reference paper of Tibshirani. Regularization could also be performed using dimension reduction as is the case with partial least squares (PLS) regression. We propose two original algorithms named sPLSDR and its non-linear kernel counterpart DKsPLSDR, by using sparse PLS regression (sPLS) based on deviance residuals. We compared their predicting performance with state-of-the-art algorithms on both simulated and real reference benchmark datasets. RESULTS: sPLSDR and DKsPLSDR compare favorably with other methods in their computational time, prediction and selectivity, as indicated by results based on benchmark datasets. Moreover, in the framework of PLS regression, they feature other useful tools, including biplots representation, or the ability to deal with missing data. Therefore, we view them as a useful addition to the toolbox of estimation and prediction methods for the widely used Cox's model in the high-dimensional and low-sample size settings. AVAILABILITY AND IMPLEMENTATION: The R-package plsRcox is available on the CRAN and is maintained by Frederic Bertrand. http://cran.r-project.org/web/packages/plsRcox/index.html. CONTACT: pbastien@rd.loreal.com or fbertran@math.unistra.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
  keywords = {*Algorithms,*Least-Squares Analysis,*Regression Analysis,*Software,Datasets as Topic,Gene Expression Profiling/*methods,Humans,Proportional Hazards Models,Sample Size,Survival Rate}
}

@article{bertrand2021,
  title = {Fitting and {{Cross-Validating Cox Models}} to {{Censored Big Data With Missing Values Using Extensions}} of {{Partial Least Squares Regression Models}}},
  author = {Bertrand, F. and {Maumy-Bertrand}, M.},
  year = {2021},
  journal = {Front Big Data},
  volume = {4},
  pages = {684794},
  issn = {2624-909X (Electronic) 2624-909X (Linking)},
  doi = {10.3389/fdata.2021.684794},
  abstract = {Fitting Cox models in a big data context -on a massive scale in terms of volume, intensity, and complexity exceeding the capacity of usual analytic tools-is often challenging. If some data are missing, it is even more difficult. We proposed algorithms that were able to fit Cox models in high dimensional settings using extensions of partial least squares regression to the Cox models. Some of them were able to cope with missing data. We were recently able to extend our most recent algorithms to big data, thus allowing to fit Cox model for big data with missing values. When cross-validating standard or extended Cox models, the commonly used criterion is the cross-validated partial loglikelihood using a naive or a van Houwelingen scheme -to make efficient use of the death times of the left out data in relation to the death times of all the data. Quite astonishingly, we will show, using a strong simulation study involving three different data simulation algorithms, that these two cross-validation methods fail with the extensions, either straightforward or more involved ones, of partial least squares regression to the Cox model. This is quite an interesting result for at least two reasons. Firstly, several nice features of PLS based models, including regularization, interpretability of the components, missing data support, data visualization thanks to biplots of individuals and variables -and even parsimony or group parsimony for Sparse partial least squares or sparse group SPLS based models, account for a common use of these extensions by statisticians who usually select their hyperparameters using cross-validation. Secondly, they are almost always featured in benchmarking studies to assess the performance of a new estimation technique used in a high dimensional or big data context and often show poor statistical properties. We carried out a vast simulation study to evaluate more than a dozen of potential cross-validation criteria, either AUC or prediction error based. Several of them lead to the selection of a reasonable number of components. Using these newly found cross-validation criteria to fit extensions of partial least squares regression to the Cox model, we performed a benchmark reanalysis that showed enhanced performances of these techniques. In addition, we proposed sparse group extensions of our algorithms and defined a new robust measure based on the Schmid score and the R coefficient of determination for least absolute deviation: the integrated R Schmid Score weighted. The R-package used in this article is available on the CRAN, http://cran.r-project.org/web/packages/plsRcox/index.html. The R package bigPLS will soon be available on the CRAN and, until then, is available on Github https://github.com/fbertran/bigPLS.},
  pmcid = {PMC8591675},
  keywords = {big data and analytics,censored data,commercial or financial relationships that could be construed as a potential,conflict of interest.,Cox models,cross validation,kernel techniques,partial least squares,sparse partial least squares discriminant analysis,sparse partial least squares regression}
}

@article{boulesteix2007,
  title = {Partial Least Squares: A Versatile Tool for the Analysis of High-Dimensional Genomic Data},
  shorttitle = {Partial Least Squares},
  author = {Boulesteix, Anne-Laure and Strimmer, Korbinian},
  year = {2007},
  month = jan,
  journal = {Briefings in Bioinformatics},
  volume = {8},
  number = {1},
  pages = {32--44},
  issn = {1467-5463},
  doi = {10.1093/bib/bbl016},
  abstract = {Partial least squares (PLS) is an efficient statistical regression technique that is highly suited for the analysis of genomic and proteomic data. In this article, we review both the theory underlying PLS as well as a host of bioinformatics applications of PLS. In particular, we provide a systematic comparison of the PLS approaches currently employed, and discuss analysis problems as diverse as, e.g. tumor classification from transcriptome data, identification of relevant genes, survival analysis and modeling of gene networks and transcription factor activities.}
}

@article{boulesteix2010,
  title = {Testing the Additional Predictive Value of High-Dimensional Molecular Data},
  author = {Boulesteix, A. L. and Hothorn, T.},
  year = {2010},
  month = feb,
  journal = {BMC Bioinformatics},
  volume = {11},
  pages = {78},
  issn = {1471-2105 (Electronic) 1471-2105 (Linking)},
  doi = {10.1186/1471-2105-11-78},
  abstract = {BACKGROUND: While high-dimensional molecular data such as microarray gene expression data have been used for disease outcome prediction or diagnosis purposes for about ten years in biomedical research, the question of the additional predictive value of such data given that classical predictors are already available has long been under-considered in the bioinformatics literature. RESULTS: We suggest an intuitive permutation-based testing procedure for assessing the additional predictive value of high-dimensional molecular data. Our method combines two well-known statistical tools: logistic regression and boosting regression. We give clear advice for the choice of the only method parameter (the number of boosting iterations). In simulations, our novel approach is found to have very good power in different settings, e.g. few strong predictors or many weak predictors. For illustrative purpose, it is applied to the two publicly available cancer data sets. CONCLUSIONS: Our simple and computationally efficient approach can be used to globally assess the additional predictive power of a large number of candidate predictors given that a few clinical covariates or a known prognostic index are already available. It is implemented in the R package "globalboosttest" which is publicly available from R-forge and will be sent to the CRAN as soon as possible.},
  pmcid = {PMC2837029},
  keywords = {Computational Biology/*methods,Gene Expression Profiling/*methods,Humans,Logistic Models,Neoplasms/genetics,Oligonucleotide Array Sequence Analysis/methods}
}

@article{cao2020,
  title = {Environmental Pollutants Damage Airway Epithelial Cell Cilia: {{Implications}} for the Prevention of Obstructive Lung Diseases},
  shorttitle = {Environmental Pollutants Damage Airway Epithelial Cell Cilia},
  author = {Cao, Yu and Chen, Miao and Dong, Dan and Xie, Songbo and Liu, Min},
  year = {2020},
  journal = {Thoracic Cancer},
  volume = {11},
  number = {3},
  pages = {505--510},
  issn = {1759-7714},
  doi = {10.1111/1759-7714.13323},
  abstract = {Mucociliary epithelium lining the upper and lower respiratory tract constitutes the first line of defense of the airway and lungs against inhaled pollutants and pathogens. The concerted beating of multiciliated cells drives mucociliary clearance. Abnormalities in both the structure and function of airway cilia have been implicated in obstructive lung diseases. Emerging evidence reveals a close correlation between lung diseases and environmental stimuli such as sulfur dioxide and tobacco particles. However, the underlying mechanism remains to be described. In this review, we emphasize the importance of airway cilia in mucociliary clearance and discuss how environmental pollutants affect the structure and function of airway cilia, thus shedding light on the function of airway cilia in preventing obstructive lung diseases and revealing the negative effects of environmental pollutants on human health.},
  langid = {english},
  keywords = {Airway epithelium,environmental pollutant,lung disease,mucociliary clearance}
}

@article{chen2009,
  title = {{{ToppGene Suite}} for Gene List Enrichment Analysis and Candidate Gene Prioritization},
  author = {Chen, J. and Bardes, E. E. and Aronow, B. J. and Jegga, A. G.},
  year = {2009},
  month = jul,
  journal = {Nucleic Acids Res},
  volume = {37},
  number = {Web Server Issue},
  pages = {W305-11},
  issn = {1362-4962 (Electronic) 0305-1048 (Linking)},
  doi = {10.1093/nar/gkp427},
  abstract = {ToppGene Suite (http://toppgene.cchmc.org; this web site is free and open to all users and does not require a login to access) is a one-stop portal for (i) gene list functional enrichment, (ii) candidate gene prioritization using either functional annotations or network analysis and (iii) identification and prioritization of novel disease candidate genes in the interactome. Functional annotation-based disease candidate gene prioritization uses a fuzzy-based similarity measure to compute the similarity between any two genes based on semantic annotations. The similarity scores from individual features are combined into an overall score using statistical meta-analysis. A P-value of each annotation of a test gene is derived by random sampling of the whole genome. The protein-protein interaction network (PPIN)-based disease candidate gene prioritization uses social and Web networks analysis algorithms (extended versions of the PageRank and HITS algorithms, and the K-Step Markov method). We demonstrate the utility of ToppGene Suite using 20 recently reported GWAS-based gene-disease associations (including novel disease genes) representing five diseases. ToppGene ranked 19 of 20 (95\%) candidate genes within the top 20\%, while ToppNet ranked 12 of 16 (75\%) candidate genes among the top 20\%.},
  pmcid = {PMC2703978},
  keywords = {*Genes,*Software,Animals,Animals Disease/*genetics *Genes Humans Internet Mice Protein Interaction Mapping Proteins/genetics *Software,Disease/*genetics,Humans,Internet,Mice,Protein Interaction Mapping,Proteins/genetics}
}

@article{csala2020,
  title = {Multiset Sparse Partial Least Squares Path Modeling for High Dimensional Omics Data Analysis},
  author = {Csala, Attila and Zwinderman, Aeilko H. and Hof, Michel H.},
  year = {2020},
  month = jan,
  journal = {BMC bioinformatics},
  volume = {21},
  number = {1},
  pages = {9},
  issn = {1471-2105},
  doi = {10.1186/s12859-019-3286-3},
  abstract = {BACKGROUND: Recent technological developments have enabled the measurement of a plethora of biomolecular data from various omics domains, and research is ongoing on statistical methods to leverage these omics data to better model and understand biological pathways and genetic architectures of complex phenotypes. Current reviews report that the simultaneous analysis of multiple (i.e. three or more) high dimensional omics data sources is still challenging and suitable statistical methods are unavailable. Often mentioned challenges are the lack of accounting for the hierarchical structure between omics domains and the difficulty of interpretation of genomewide results. This study is motivated to address these challenges. We propose multiset sparse Partial Least Squares path modeling (msPLS), a generalized penalized form of Partial Least Squares path modeling, for the simultaneous modeling of biological pathways across multiple omics domains. msPLS simultaneously models the effect of multiple molecular markers, from multiple omics domains, on the variation of multiple phenotypic variables, while accounting for the relationships between data sources, and provides sparse results. The sparsity in the model helps to provide interpretable results from analyses of hundreds of thousands of biomolecular variables. RESULTS: With simulation studies, we quantified the ability of msPLS to discover associated variables among high dimensional data sources. Furthermore, we analysed high dimensional omics datasets to explore biological pathways associated with Marfan syndrome and with Chronic Lymphocytic Leukaemia. Additionally, we compared the results of msPLS to the results of Multi-Omics Factor Analysis (MOFA), which is an alternative method to analyse this type of data. CONCLUSIONS: msPLS is an multiset multivariate method for the integrative analysis of multiple high dimensional omics data sources. It accounts for the relationship between multiple high dimensional data sources while it provides interpretable results through its sparse solutions. The biomarkers found by msPLS in the omics datasets can be interpreted in terms of biological pathways associated with the pathophysiology of Marfan syndrome and of Chronic Lymphocytic Leukaemia. Additionally, msPLS outperforms MOFA in terms of variation explained in the chronic lymphocytic leukaemia dataset while it identifies the two most important clinical markers for Chronic Lymphocytic Leukaemia AVAILABILITY: http://uva.csala.me/mspls.https://github.com/acsala/2018\_msPLS.},
  langid = {english},
  pmcid = {PMC6953292},
  pmid = {31918677},
  keywords = {Genomics,High dimensional omics data,Humans,Least-Squares Analysis,Leukemia; Lymphocytic; Chronic; B-Cell,Marfan Syndrome,Multivariate analysis,Multivariate Analysis,Partial least squares,Proteomics,User-Computer Interface}
}

@article{davenport2021,
  title = {{{miR-31 Displays Subtype Specificity}} in {{Lung Cancer}}},
  author = {Davenport, Mackenzie L. and Echols, John B. and Silva, Austin D. and Anderson, Joshua C. and Owens, Philip and Yates, Clayton and Wei, Qing and Harada, Shuko and Hurst, Douglas R. and Edmonds, Mick D.},
  year = {2021},
  month = apr,
  journal = {Cancer Research},
  volume = {81},
  number = {8},
  pages = {1942--1953},
  issn = {1538-7445},
  doi = {10.1158/0008-5472.CAN-20-2769},
  abstract = {miRNA rarely possess pan-oncogenic or tumor-suppressive properties. Most miRNAs function under tissue-specific contexts, acting as either tumor suppressors in one tissue, promoting oncogenesis in another, or having no apparent role in the regulation of processes associated with the hallmarks of cancer. What has been less clear is the role of miRNAs within cell types of the same tissue and the ability within each cell type to contribute to oncogenesis. In this study, we characterize the role of one such tissue-specific miRNA, miR-31, recently identified as the most oncogenic miRNA in lung adenocarcinoma, across the histologic spectrum of human lung cancer. Compared with normal lung tissue, miR-31 was overexpressed in patient lung adenocarcinoma, squamous cell carcinoma, and large-cell neuroendocrine carcinoma, but not small-cell carcinoma or carcinoids. miR-31 promoted tumor growth in mice of xenografted human adenocarcinoma and squamous cell carcinoma cell lines, but not in large- or small-cell carcinoma lines. While miR-31 did not promote primary tumor growth of large- and small-cell carcinoma, it did promote spontaneous metastasis. Mechanistically, miR-31 altered distinct cellular signaling programs within each histologic subtype, resulting in distinct phenotypic differences. This is the first report distinguishing diverse functional roles for this miRNA across the spectrum of lung cancers and suggests that miR-31 has broad clinical value in human lung malignancy. SIGNIFICANCE: These findings demonstrate the oncogenic properties of miR-31 in specific subtypes of lung cancer and highlight it as a potential therapeutic target in these subtypes. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/81/8/1942/F1.large.jpg.},
  langid = {english},
  pmcid = {PMC8137562},
  pmid = {33558335},
  keywords = {Adenocarcinoma of Lung,Animals,Carcinoma; Adenosquamous,Carcinoma; Large Cell,Carcinoma; Neuroendocrine,Carcinoma; Squamous Cell,Cell Line; Tumor,Cell Proliferation,Databases; Genetic,Female,Humans,Liver Neoplasms,Lung,Lung Neoplasms,Male,Mice,Mice; Nude,MicroRNAs,Neoplasm Metastasis,Neoplasm Transplantation,Organ Specificity,Signal Transduction,Small Cell Lung Carcinoma,Tumor Suppressor Proteins}
}

@article{deng2017,
  title = {{{FirebrowseR}}: An {{R}} Client to the {{Broad Institute}}'s {{Firehose Pipeline}}},
  author = {Deng, M. and Bragelmann, J. and Kryukov, I. and {Saraiva-Agostinho}, N. and Perner, S.},
  year = {2017},
  journal = {Database (Oxford)},
  volume = {2017},
  issn = {1758-0463 (Electronic) 1758-0463 (Linking)},
  doi = {10.1093/database/baw160},
  abstract = {With its Firebrowse service (http://firebrowse.org/) the Broad Institute is making large-scale multi-platform omics data analysis results publicly available through a Representational State Transfer (REST) Application Programmable Interface (API). Querying this database through an API client from an arbitrary programming environment is an essential task, allowing other developers and researchers to focus on their analysis and avoid data wrangling. Hence, as a first result, we developed a workflow to automatically generate, test and deploy such clients for rapid response to API changes. Its underlying infrastructure, a combination of free and publicly available web services, facilitates the development of API clients. It decouples changes in server software from the client software by reacting to changes in the RESTful service and removing direct dependencies on a specific implementation of an API. As a second result, FirebrowseR, an R client to the Broad Institute's RESTful Firehose Pipeline, is provided as a working example, which is built by the means of the presented workflow. The package's features are demonstrated by an example analysis of cancer gene expression data.Database URL: https://github.com/mariodeng/.},
  pmcid = {PMC5216271},
  keywords = {*Databases; Genetic,*Gene Expression Regulation; Neoplastic,*Internet,*Neoplasms/genetics/metabolism,*Programming Languages,*User-Computer Interface,Animals,Animals *Databases,Genetic *Gene Expression Regulation,Humans,Neoplastic Humans *Internet *Neoplasms/genetics/metabolism *Programming Languages *User-Computer Interface}
}

@article{edmonds2016,
  title = {{{MicroRNA-31}} Initiates Lung Tumorigenesis and Promotes Mutant {{KRAS-driven}} Lung Cancer},
  author = {Edmonds, Mick D. and Boyd, Kelli L. and Moyo, Tamara and Mitra, Ramkrishna and Duszynski, Robert and Arrate, Maria Pia and Chen, Xi and Zhao, Zhongming and Blackwell, Timothy S. and Andl, Thomas and Eischen, Christine M.},
  year = {2016},
  month = jan,
  journal = {The Journal of Clinical Investigation},
  volume = {126},
  number = {1},
  pages = {349--364},
  issn = {1558-8238},
  doi = {10.1172/JCI82720},
  abstract = {MicroRNA (miR) are important regulators of gene expression, and aberrant miR expression has been linked to oncogenesis; however, little is understood about their contribution to lung tumorigenesis. Here, we determined that miR-31 is overexpressed in human lung adenocarcinoma and this overexpression independently correlates with decreased patient survival. We developed a transgenic mouse model that allows for lung-specific expression of miR-31 to test the oncogenic potential of miR-31 in the lung. Using this model, we observed that miR-31 induction results in lung hyperplasia, followed by adenoma formation and later adenocarcinoma development. Moreover, induced expression of miR-31 in mice cooperated with mutant KRAS to accelerate lung tumorigenesis. We determined that miR-31 regulates lung epithelial cell growth and identified 6 negative regulators of RAS/MAPK signaling as direct targets of miR-31. Our study distinguishes miR-31 as a driver of lung tumorigenesis that promotes mutant KRAS-mediated oncogenesis and reveals that miR-31 directly targets and reduces expression of negative regulators of RAS/MAPK signaling.},
  langid = {english},
  pmcid = {PMC4701567},
  pmid = {26657862},
  keywords = {Adenocarcinoma,Adenocarcinoma of Lung,Animals,Cell Line; Tumor,Female,Humans,Lung Neoplasms,Male,MAP Kinase Signaling System,Mice,MicroRNAs,Mutation,NIH 3T3 Cells,Proto-Oncogene Proteins p21(ras),ras Proteins}
}

@article{graw2021,
  title = {Multi-Omics Data Integration Considerations and Study Design for Biological Systems and Disease},
  author = {Graw, S. and Chappell, K. and Washam, C. L. and Gies, A. and Bird, J. and Robeson, M. S. and Byrum, S. D.},
  year = {2021},
  month = apr,
  journal = {Mol Omics},
  volume = {17},
  number = {2},
  pages = {170--185},
  issn = {2515-4184 (Electronic) 2515-4184 (Linking)},
  doi = {10.1039/d0mo00041h},
  abstract = {With the advancement of next-generation sequencing and mass spectrometry, there is a growing need for the ability to merge biological features in order to study a system as a whole. Features such as the transcriptome, methylome, proteome, histone post-translational modifications and the microbiome all influence the host response to various diseases and cancers. Each of these platforms have technological limitations due to sample preparation steps, amount of material needed for sequencing, and sequencing depth requirements. These features provide a snapshot of one level of regulation in a system. The obvious next step is to integrate this information and learn how genes, proteins, and/or epigenetic factors influence the phenotype of a disease in context of the system. In recent years, there has been a push for the development of data integration methods. Each method specifically integrates a subset of omics data using approaches such as conceptual integration, statistical integration, model-based integration, networks, and pathway data integration. In this review, we discuss considerations of the study design for each data feature, the limitations in gene and protein abundance and their rate of expression, the current data integration methods, and microbiome influences on gene and protein expression. The considerations discussed in this review should be regarded when developing new algorithms for integrating multi-omics data.},
  pmcid = {PMC8058243},
  keywords = {*Genomics,*Proteomics,Algorithms,Epigenomics,High-Throughput Nucleotide Sequencing,Humans,Proteome/*genetics,Transcriptome/*genetics}
}

@article{heo2021,
  title = {Integrative {{Multi-Omics Approaches}} in {{Cancer Research}}: {{From Biological Networks}} to {{Clinical Subtypes}}},
  author = {Heo, Y. J. and Hwa, C. and Lee, G. H. and Park, J. M. and An, J. Y.},
  year = {2021},
  month = jul,
  journal = {Mol Cells},
  volume = {44},
  number = {7},
  pages = {433--443},
  issn = {0219-1032 (Electronic) 1016-8478 (Linking)},
  doi = {10.14348/molcells.2021.0042},
  abstract = {Multi-omics approaches are novel frameworks that integrate multiple omics datasets generated from the same patients to better understand the molecular and clinical features of cancers. A wide range of emerging omics and multi-view clustering algorithms now provide unprecedented opportunities to further classify cancers into subtypes, improve the survival prediction and therapeutic outcome of these subtypes, and understand key pathophysiological processes through different molecular layers. In this review, we overview the concept and rationale of multi-omics approaches in cancer research. We also introduce recent advances in the development of multi-omics algorithms and integration methods for multiple-layered datasets from cancer patients. Finally, we summarize the latest findings from large-scale multi-omics studies of various cancers and their implications for patient subtyping and drug development.},
  pmcid = {PMC8334347},
  keywords = {Biomedical Research/*methods,cancer research,Computational Biology/*methods,genomics,Humans,Metabolomics/*methods,multi-omics approach,Neoplasms/mortality/*therapy,proteogenomics,proteomics,Survival Analysis,systems biology}
}

@article{hillebrand2021,
  title = {Bagging Weak Predictors},
  author = {Hillebrand, Eric and Lukas, Manuel and Wei, Wei},
  year = {2021},
  journal = {Int. J. Forecasting},
  volume = {37},
  number = {1},
  pages = {237--254}
}

@article{jensen2017,
  title = {The {{NCI Genomic Data Commons}} as an Engine for Precision Medicine},
  author = {Jensen, M. A. and Ferretti, V. and Grossman, R. L. and Staudt, L. M.},
  year = {2017},
  month = jul,
  journal = {Blood},
  volume = {130},
  number = {4},
  pages = {453--459},
  issn = {1528-0020 (Electronic) 0006-4971 (Linking)},
  doi = {10.1182/blood-2017-03-735654},
  abstract = {The National Cancer Institute Genomic Data Commons (GDC) is an information system for storing, analyzing, and sharing genomic and clinical data from patients with cancer. The recent high-throughput sequencing of cancer genomes and transcriptomes has produced a big data problem that precludes many cancer biologists and oncologists from gleaning knowledge from these data regarding the nature of malignant processes and the relationship between tumor genomic profiles and treatment response. The GDC aims to democratize access to cancer genomic data and to foster the sharing of these data to promote precision medicine approaches to the diagnosis and treatment of cancer.},
  pmcid = {PMC5533202},
  keywords = {*Databases; Genetic,*Precision Medicine,*Software,Humans,National Cancer Institute (U.S.),Neoplasms/*genetics,United States}
}

@article{kalluri2016,
  title = {The Biology and Function of Fibroblasts in Cancer},
  author = {Kalluri, Raghu},
  year = {2016},
  month = aug,
  journal = {Nature Reviews. Cancer},
  volume = {16},
  number = {9},
  pages = {582--598},
  issn = {1474-1768},
  doi = {10.1038/nrc.2016.73},
  abstract = {Among all cells, fibroblasts could be considered the cockroaches of the human body. They survive severe stress that is usually lethal to all other cells, and they are the only normal cell type that can be live-cultured from post-mortem and decaying tissue. Their resilient adaptation may reside in their intrinsic survival programmes and cellular plasticity. Cancer is associated with fibroblasts at all stages of disease progression, including metastasis, and they are a considerable component of the general host response to tissue damage caused by cancer cells. Cancer-associated fibroblasts (CAFs) become synthetic machines that produce many different tumour components. CAFs have a role in creating extracellular matrix (ECM) structure and metabolic and immune reprogramming of the tumour microenvironment with an impact on adaptive resistance to chemotherapy. The pleiotropic actions of CAFs on tumour cells are probably reflective of them being a heterogeneous and plastic population with context-dependent influence on cancer.},
  langid = {english},
  pmid = {27550820},
  keywords = {Cell Differentiation,Cell Lineage,Cytokines,Disease Progression,Drug Resistance; Neoplasm,Epigenesis; Genetic,Extracellular Matrix,Fibroblasts,Fibrosis,Forecasting,Humans,Mesenchymal Stem Cells,Neoplasm Metastasis,Neoplasm Proteins,Neoplasms,Neovascularization; Pathologic,Stromal Cells,Tumor Microenvironment,Wound Healing}
}

@article{kampstra2008,
  title = {Beanplot: {{A}} Boxplot Alternative for Visual Comparison of Distributions},
  author = {Kampstra, Peter},
  year = {2008},
  journal = {Journal of statistical software},
  volume = {28},
  number = {1},
  pages = {1--9},
  issn = {1548-7660}
}

@article{kim2020,
  title = {{{RICTOR Amplification Promotes NSCLC Cell Proliferation}} through {{Formation}} and {{Activation}} of {{mTORC2}} at the {{Expense}} of {{mTORC1}}},
  author = {Kim, Laura C. and Rhee, Christopher H. and Chen, Jin},
  year = {2020},
  month = nov,
  journal = {Molecular cancer research: MCR},
  volume = {18},
  number = {11},
  pages = {1675--1684},
  issn = {1557-3125},
  doi = {10.1158/1541-7786.MCR-20-0262},
  abstract = {Non-small cell lung cancer (NSCLC) is characterized by genomic alterations, yet a targetable mutation has not been discovered in nearly half of all patients. Recent studies have identified amplification of RICTOR, an mTORC2-specific cofactor, as a novel actionable target in NSCLC. mTORC2 is one of two distinct mTOR complexes to sense environmental cues and regulate a variety of cellular processes, including cell growth, proliferation, and metabolism, all of which promote tumorigenesis when aberrantly regulated. Interestingly, other components of mTORC2 are not coamplified with RICTOR in human lung cancer, raising the question as to whether RICTOR amplification-induced changes are dependent on mTORC2 function. To model RICTOR amplification, we overexpressed Rictor using the Cas9 Synergistic Activation Mediator system. Overexpression of Rictor increased mTORC2 integrity and signaling, but at the expense of mTORC1, suggesting that overexpressed Rictor recruits common components away from mTORC1. Additionally, Rictor overexpression increases the proliferation and growth of NSCLC 3D cultures and tumors in vivo. Conversely, knockout of RICTOR leads to decreased mTORC2 formation and activity, but increased mTORC1 function. Because Rictor has mTOR-dependent and -independent functions, we also knocked out mLST8, a shared mTOR cofactor but is specifically required for mTORC2 function. Inducible loss of mLST8 in RICTOR-amplified NSCLC cells inhibited mTORC2 integrity and signaling, tumor cell proliferation, and tumor growth. Collectively, these data identify a mechanism for Rictor-driven tumor progression and provide further rationale for the development of an mTORC2-specific inhibitor. IMPLICATIONS: RICTOR amplification drives NSCLC proliferation through formation of mTORC2, suggesting mTORC2-specific inhibition could be a beneficial therapeutic option.},
  langid = {english},
  pmcid = {PMC7642103},
  pmid = {32801163},
  keywords = {Animals,Carcinoma; Non-Small-Cell Lung,Cell Proliferation,Humans,Lung Neoplasms,Mechanistic Target of Rapamycin Complex 1,Mice; Nude,Rapamycin-Insensitive Companion of mTOR Protein,Transfection}
}

@article{kotsiantis2011,
  title = {Combining Bagging, Boosting, Rotation Forest and Random Subspace Methods},
  author = {Kotsiantis, Sotiris},
  year = {2011},
  month = mar,
  journal = {Artificial Intelligence Review},
  volume = {35},
  number = {3},
  pages = {223--240},
  issn = {0269-2821, 1573-7462},
  doi = {10.1007/s10462-010-9192-8},
  abstract = {Bagging, boosting, rotation forest and random subspace methods are well known re-sampling ensemble methods that generate and combine a diversity of learners using the same learning algorithm for the base-classifiers. Boosting and rotation forest algorithms are considered stronger than bagging and random subspace methods on noise-free data. However, there are strong empirical indications that bagging and random subspace methods are much more robust than boosting and rotation forest in noisy settings. For this reason, in this work we built an ensemble of bagging, boosting, rotation forest and random subspace methods ensembles with 6 sub-classifiers in each one and then a voting methodology is used for the final prediction. We performed a comparison with simple bagging, boosting, rotation forest and random subspace methods ensembles with 25 sub-classifiers, as well as other well known combining methods, on standard benchmark datasets and the proposed technique had better accuracy in most cases.},
  langid = {english}
}

@article{lee2018,
  title = {Location of Stage {{I}}\textendash{{III}} Non-small Cell Lung Cancer and Survival Rate: {{Systematic}} Review and Meta-analysis},
  shorttitle = {Location of Stage {{I}}\textendash{{III}} Non-small Cell Lung Cancer and Survival Rate},
  author = {Lee, Hyun Woo and Lee, Chang-Hoon and Park, Young Sik},
  year = {2018},
  month = dec,
  journal = {Thoracic Cancer},
  volume = {9},
  number = {12},
  pages = {1614--1622},
  issn = {1759-7706},
  doi = {10.1111/1759-7714.12869},
  abstract = {Background The association between the location of non-small cell lung cancer (NSCLC) and prognosis is a debated issue. Some studies have provided evidence of better prognosis of upper lobe tumors than lower to middle lobe tumors, while other studies have reported contrasting conclusions. The aim of this study was to further assess this association through a systematic review and meta-analysis. Methods Medline, Embase, and the Cochrane Central Register of Controlled Trials were searched up to 27 January 2017. Patients pathologically diagnosed with stage I\textendash III NSCLC with three or five-year survival data were included. The main meta-analysis compared differences in survival rates according to the primary tumor location using the Mantel\textendash Haenszel method with a random effect model. Sensitivity analysis was conducted according to lymph node metastasis, tumor node metastasis stage, staging method, and treatment modality. Results Ten clinical studies and 35 570 patients were recruited. Patients with tumors in the upper lobes had a higher rate of five-year survival compared to those with tumors in non-upper lobes (odds ratio [OR] 1.31, 95\% confidence interval [CI] 1.15\textendash 1.49). Similarly, the three-year survival rate was high in patients with tumors in the upper lobes (OR 1.99, 95\% CI 1.02\textendash 3.86) and low in those with lower lobe tumors (OR 0.31, 95\% CI 0.12\textendash 0.77). Conclusions Stage I\textendash III NSCLC located in the upper lobes showed higher five-year survival rates compared to other tumor locations.},
  pmcid = {PMC6275821},
  pmid = {30259691}
}

@article{li2016,
  title = {Identification of {{TRA2B-DNAH5}} Fusion as a Novel Oncogenic Driver in Human Lung Squamous Cell Carcinoma},
  author = {Li, Fei and Fang, Zhaoyuan and Zhang, Jian and Li, Chen and Liu, Hongyan and Xia, Jufeng and Zhu, Hongwen and Guo, Chenchen and Qin, Zhen and Li, Fuming and Han, Xiangkun and Wang, Yuetong and Feng, Yan and Wang, Ye and Zhang, Wenjing and Wang, Zuoyun and Jin, Yujuan and Sun, Yihua and Wei, Wenyi and Zeng, Rong and Chen, Haiquan and Ji, Hongbin},
  year = {2016},
  month = oct,
  journal = {Cell Research},
  volume = {26},
  number = {10},
  pages = {1149--1164},
  issn = {1748-7838},
  doi = {10.1038/cr.2016.111},
  abstract = {Lung squamous cell carcinoma (SCC) is one of the major subtypes of lung cancer. Our current knowledge of oncogenic drivers in this specific subtype of lung cancer is largely limited compared with lung adenocarcinoma (ADC). Through exon array analyses, molecular analyses and functional studies, we here identify the TRA2B-DNAH5 fusion as a novel oncogenic driver in lung SCC. We found that this gene fusion occurs exclusively in lung SCC (3.1\%, 5/163), but not in lung ADC (0/119). Through mechanistic studies, we further revealed that this TRA2B-DNAH5 fusion promotes lung SCC malignant progression through regulating a SIRT6-ERK1/2-MMP1 signaling axis. We show that inhibition of ERK1/2 activation using selumetinib efficiently inhibits the growth of lung SCC with TRA2B-DNAH5 fusion expression. These findings improve our current knowledge of oncogenic drivers in lung SCC and provide a potential therapeutic strategy for lung SCC patients with TRA2B-DNAH5 fusion.},
  langid = {english},
  pmcid = {PMC5113306},
  pmid = {27670699},
  keywords = {Animals,Axonemal Dyneins,Benzimidazoles,Carcinoma; Squamous Cell,Cell Line,Cell Proliferation,Gene Fusion,Humans,Intracellular Signaling Peptides and Proteins,Lung Neoplasms,Matrix Metalloproteinase 1,Mice,Mice; Nude,Mitogen-Activated Protein Kinase 1,Mitogen-Activated Protein Kinase 3,Nerve Tissue Proteins,Oncogene Proteins; Fusion,Serine-Arginine Splicing Factors,Signal Transduction,Sirtuins,T-Lymphoma Invasion and Metastasis-inducing Protein 1,Transplantation; Heterologous}
}

@article{lu2020,
  title = {Overexpression of P-{{Akt}}, p-{{mTOR}} and p-{{eIF4E}} Proteins Associates with Metastasis and Unfavorable Prognosis in Non-Small Cell Lung Cancer},
  author = {Lu, Junmi and Zang, Hongjing and Zheng, Hongmei and Zhan, Yuting and Yang, Yang and Zhang, Yuting and Liu, Sile and Feng, Juan and Wen, Qiuyuan and Long, Mengping and Fan, Songqing},
  year = {2020},
  month = feb,
  journal = {PLOS ONE},
  volume = {15},
  number = {2},
  pages = {e0227768},
  publisher = {{Public Library of Science}},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0227768},
  abstract = {The Akt (protein kinase B)/mammalian target of rapamycin (mTOR) pathway, which is dysregulated in various cancers, controls the assembly of eukaryotic translation initiation factor 4F (eIF4E) complex. However, whether aberrant expression of phosphorylated Akt (p-Akt), phosphorylated mTOR (p-mTOR) and phosphorylated eIF4E (p-eIF4E) is associated with clinicopathological characteristics in surgically resected non-small cell lung cancer (NSCLC) has been rarely reported. Here, we investigated expression of p-Akt, p-mTOR and p-eIF4E proteins in NSCLC by immunohistochemistry and evaluated their correlation with clinicopathological characteristics and prognostic significance. The results showed that the positive percentage of p-Akt, p-mTOR and p-eIF4E was higher in NSCLC. Additionally, p-mTOR and p-eIF4E was dramatically higher in lung adenocarcinoma (both P{$<$}0.05). Most importantly, NSCLC patients with lymph node metastasis had significantly elevated expression of p-Akt, p-mTOR and p-eIF4E (all P{$<$}0.05). Positive expression of p-Akt, and any positive expression of p-Akt, p-mTOR and p-eIF4E proteins were positively correlated with clinical stages (both P{$<$}0.05). Spearman's rank correlation test revealed that expression of p-Akt was correlated with p-eIF4E and p-mTOR (r = 0.107, P = 0.047; r = 0.287, P{$<$}0.001, respectively). Also, p-eIF4E had positive correlation with p-mTOR (r = 0.265, P{$<$}0.001). Furthermore, NSCLC patients with increased expression of p-Akt, p-mTOR and p-eIF4E, and any positive expression of above three proteins had lower overall survival rates (all P{$<$}0.05). Multivariate Cox regression analysis further indicated thatp-eIF4E was an independent prognostic factor for NSCLC patients (P = 0.046). Taken together, overexpression of p-Akt, p-mTOR and p-eIF4E proteins is associated with metastasis and poor prognosis of NSCLC patients after surgical resection, and positive expression of p-eIF4E protein may act as an independent unfavorable prognostic biomarker for overall survival of NSCLC patients.},
  langid = {english},
  keywords = {Adenocarcinoma,Adenocarcinoma of the lung,Cytoplasmic staining,Immunohistochemistry techniques,Non-small cell lung cancer,Prognosis,Protein expression,Squamous cell lung carcinoma}
}

@inproceedings{maclin1997,
  title = {An Empirical Evaluation of Bagging and Boosting},
  booktitle = {Proceedings of the Fourteenth National Conference on Artificial Intelligence and Ninth Conference on {{Innovative}} Applications of Artificial Intelligence},
  author = {Maclin, Richard and Opitz, David},
  year = {1997},
  month = jul,
  series = {{{AAAI}}'97/{{IAAI}}'97},
  pages = {546--551},
  publisher = {{AAAI Press}},
  address = {{Providence, Rhode Island}},
  abstract = {An ensemble consists of a set of independently trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman 1996a) and Boosting (Freund \& Schapire 1996) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods using both neural networks and decision trees as our classification algorithms. Our results clearly show two important facts. The first is that even though Bagging almost always produces a better classifier than any of its individual component classifiers and is relatively impervious to overfitting, it does not generalize any better than a baseline neural-network ensemble method. The second is that Boosting is a powerful technique that can usually produce better ensembles than Bagging; however, it is more susceptible to noise and can quickly overfit a data set.},
  isbn = {978-0-262-51095-0}
}

@article{matsushima2011,
  title = {{{MiRNA-205}} Modulates Cellular Invasion and Migration via Regulating Zinc Finger {{E-box}} Binding Homeobox 2 Expression in Esophageal Squamous Cell Carcinoma Cells},
  author = {Matsushima, Kayoko and Isomoto, Hajime and Yamaguchi, Naoyuki and Inoue, Naoki and Machida, Haruhisa and Nakayama, Toshiyuki and Hayashi, Tomayoshi and Kunizaki, Masaki and Hidaka, Shigekazu and Nagayasu, Takeshi and Nakashima, Masahiro and Ujifuku, Kenta and Mitsutake, Norisato and Ohtsuru, Akira and Yamashita, Shunichi and Korpal, Manav and Kang, Yibin and Gregory, Philip A. and Goodall, Gregory J. and Kohno, Shigeru and Nakao, Kazuhiko},
  year = {2011},
  month = mar,
  journal = {Journal of Translational Medicine},
  volume = {9},
  pages = {30},
  issn = {1479-5876},
  doi = {10.1186/1479-5876-9-30},
  abstract = {BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is often diagnosed at later stages until they are incurable. MicroRNA (miR) is a small, non-coding RNA that negatively regulates gene expression mainly via translational repression. Accumulating evidence indicates that deregulation of miR is associated with human malignancies including ESCC. The aim of this study was to identify miR that could be specifically expressed and exert distinct biological actions in ESCC. METHODS: Total RNA was extracted from ESCC cell lines, OE21 and TE10, and a non-malignant human esophageal squamous cell line, Het-1A, and subjected to microarray analysis. Expression levels of miR that showed significant differences between the 2 ESCC and Het-1A cells based on the comprehensive analysis were analyzed by the quantitative reverse transcriptase (RT)-PCR method. Then, functional analyses, including cellular proliferation, apoptosis and Matrigel invasion and the wound healing assay, for the specific miR were conducted. Using ESCC tumor samples and paired surrounding non-cancerous tissue obtained endoscopically, the association with histopathological differentiation was examined with quantitative RT-PCR. RESULTS: Based on the miR microarray analysis, there were 14 miRs that showed significant differences (more than 2-fold) in expression between the 2 ESCC cells and non-malignant Het-1A. Among the significantly altered miRs, miR-205 expression levels were exclusively higher in 5 ESCC cell lines examined than any other types of malignant cell lines and Het-1A. Thus, miR-205 could be a specific miR in ESCC. Modulation of miR-205 expression by transfection with its precursor or anti-miR-205 inhibitor did not affect ESCC cell proliferation and apoptosis, but miR-205 was found to be involved in cell invasion and migration. Western blot revealed that knockdown of miR-205 expression in ESCC cells substantially enhanced expression of zinc finger E-box binding homeobox 2, accompanied by reduction of E-cadherin, a regulator of epithelial mesenchymal transition. The miR-205 expression levels were not associated with histological differentiation of human ESCC. CONCLUSIONS: These results imply that miR-205 is an ESCC-specific miR that exerts tumor-suppressive activities with EMT inhibition by targeting ZEB2.},
  langid = {english},
  pmcid = {PMC3076245},
  pmid = {21426561},
  keywords = {Apoptosis,Carcinoma; Squamous Cell,Cell Differentiation,Cell Line; Tumor,Cell Movement,Cell Proliferation,Epithelial-Mesenchymal Transition,Esophageal Neoplasms,Gene Expression Profiling,Gene Expression Regulation; Neoplastic,Homeodomain Proteins,Humans,MicroRNAs,Neoplasm Invasiveness,Phenotype,Repressor Proteins,Up-Regulation,Zinc Finger E-box Binding Homeobox 2}
}

@article{mishra2022,
  title = {Swiss Knife Partial Least Squares ({{SKPLS}}): {{One}} Tool for Modelling Single Block, Multiblock, Multiway, Multiway Multiblock Including Multi-Responses and Meta Information under the {{ROSA}} Framework},
  author = {Mishra, P. and Liland, K. H.},
  year = {2022},
  month = may,
  journal = {Anal Chim Acta},
  volume = {1206},
  pages = {339786},
  issn = {1873-4324 (Electronic) 0003-2670 (Linking)},
  doi = {10.1016/j.aca.2022.339786},
  abstract = {In the domain of chemometrics and multivariate data analysis, partial least squares (PLS) modelling is a widely used technique. PLS gains its beauty by handling the high collinearity found in multivariate data by replacing highly covarying variables with common subspaces spanned by orthogonal latent variables. Furthermore, all can be achieved with simple steps of linear algebra requiring minimal computation power and time usage compared to current high-end computing and substantial hyperparameter tuning required by methods such as deep learning. PLS can be used for a wide variety of tasks, for example, single block modelling, multiblock modelling, multiway data modelling and for task such as regression and classification. Furthermore, new PLS based approaches can also incorporate meta information to improve the PLS subspace extraction. However, in the current scenario, there is a wide range of separate tools and codes available to perform different PLS tasks. Often when the user needs to perform a new PLS task, they need to start with a separate mathematical implementation of the PLS techniques. This study aims to provide a single solution, i.e., the Swiss knife PLS (SKPLS) modelling approach to enable a single mathematical implementation to perform analyses of single block, multiblock, multiway, multiblock multiway, multi-response, and incorporation of meta information in PLS modelling. It contains all that is needed for any PLS practitioner to perform both classification and regression tasks. The SKPLS backbone is the stepwise PLS strategy called response oriented sequential alternation (ROSA) which we generalize to enable all the mentioned analysis possibilities. The basic structure of the algorithm is highlighted, and some example cases of performing single block, multiblock, multiway, multiblock multiway, multi-response PLS modelling and the incorporation of meta information in PLS modelling are included.},
  keywords = {*Algorithms,*Least-Squares Analysis,competing financial interests or personal relationships that could have appeared,Data fusion,Multi-modal,Multivariate Analysis,Multiway modelling,Spectroscopy,to influence the work reported in this paper.}
}

@inproceedings{ng2015,
  title = {A {{Simple Explanation}} of {{Partial Least Squares}}},
  author = {Ng, K. S.},
  year = {2015},
  abstract = {Partial Least Squares (PLS) is a widely used technique in chemometrics, especially in the case where the number of independent variables is significantly larger than the number of data points. There are many articles on PLS [HTF01, GK86] but the mathematical details of PLS do not always come out clearly in these treatments. This paper is an attempt to describe PLS in precise and simple mathematical terms.}
}

@article{nguyen2002,
  title = {Tumor Classification by Partial Least Squares Using Microarray Gene Expression Data},
  author = {Nguyen, Danh V. and Rocke, David M.},
  year = {2002},
  month = jan,
  journal = {Bioinformatics (Oxford, England)},
  volume = {18},
  number = {1},
  pages = {39--50},
  issn = {1367-4803},
  doi = {10.1093/bioinformatics/18.1.39},
  abstract = {MOTIVATION: One important application of gene expression microarray data is classification of samples into categories, such as the type of tumor. The use of microarrays allows simultaneous monitoring of thousands of genes expressions per sample. This ability to measure gene expression en masse has resulted in data with the number of variables p(genes) far exceeding the number of samples N. Standard statistical methodologies in classification and prediction do not work well or even at all when N {$<$} p. Modification of existing statistical methodologies or development of new methodologies is needed for the analysis of microarray data. RESULTS: We propose a novel analysis procedure for classifying (predicting) human tumor samples based on microarray gene expressions. This procedure involves dimension reduction using Partial Least Squares (PLS) and classification using Logistic Discrimination (LD) and Quadratic Discriminant Analysis (QDA). We compare PLS to the well known dimension reduction method of Principal Components Analysis (PCA). Under many circumstances PLS proves superior; we illustrate a condition when PCA particularly fails to predict well relative to PLS. The proposed methods were applied to five different microarray data sets involving various human tumor samples: (1) normal versus ovarian tumor; (2) Acute Myeloid Leukemia (AML) versus Acute Lymphoblastic Leukemia (ALL); (3) Diffuse Large B-cell Lymphoma (DLBCLL) versus B-cell Chronic Lymphocytic Leukemia (BCLL); (4) normal versus colon tumor; and (5) Non-Small-Cell-Lung-Carcinoma (NSCLC) versus renal samples. Stability of classification results and methods were further assessed by re-randomization studies.},
  langid = {english},
  pmid = {11836210},
  keywords = {Colonic Neoplasms,Computational Biology,Discriminant Analysis,Female,Gene Expression Profiling,Humans,Least-Squares Analysis,Leukemia,Logistic Models,Lymphoma,Neoplasms,Oligonucleotide Array Sequence Analysis,Ovarian Neoplasms,Principal Component Analysis,Tumor Cells; Cultured}
}

@article{nieto2013,
  title = {Epithelial Plasticity: A Common Theme in Embryonic and Cancer Cells},
  shorttitle = {Epithelial Plasticity},
  author = {Nieto, M. Angela},
  year = {2013},
  month = nov,
  journal = {Science (New York, N.Y.)},
  volume = {342},
  number = {6159},
  pages = {1234850},
  issn = {1095-9203},
  doi = {10.1126/science.1234850},
  abstract = {During embryonic development, many cells are born far from their final destination and must travel long distances. To become motile and invasive, embryonic epithelial cells undergo a process of mesenchymal conversion known as epithelial-to-mesenchymal transition (EMT). Likewise, EMT can be seen in cancer cells as they leave the primary tumor and disseminate to other parts of the body to colonize distant organs and form metastases. In addition, through the reverse process (mesenchymal-to-epithelial transition), both normal and carcinoma cells revert to the epithelial phenotype to, respectively, differentiate into organs or form secondary tumors. The parallels in phenotypic plasticity in normal morphogenesis and cancer highlight the importance of studying the embryo to understand tumor progression and to aid in the design of improved therapeutic strategies.},
  langid = {english},
  pmid = {24202173},
  keywords = {Animals,Carcinoma,Cell Movement,Disease Models; Animal,Embryonic Stem Cells,Epithelial-Mesenchymal Transition,Humans,Mice,Neoplasms,Neoplastic Stem Cells}
}

@article{omran2000,
  title = {Homozygosity Mapping of a Gene Locus for Primary Ciliary Dyskinesia on Chromosome 5p and Identification of the Heavy Dynein Chain {{DNAH5}} as a Candidate Gene},
  author = {Omran, H. and H{\"a}ffner, K. and V{\"o}lkel, A. and Kuehr, J. and Ketelsen, U. P. and Ross, U. H. and Konietzko, N. and Wienker, T. and Brandis, M. and Hildebrandt, F.},
  year = {2000},
  month = nov,
  journal = {American Journal of Respiratory Cell and Molecular Biology},
  volume = {23},
  number = {5},
  pages = {696--702},
  issn = {1044-1549},
  doi = {10.1165/ajrcmb.23.5.4257},
  abstract = {Reduced mucociliary clearance in primary ciliary dyskinesia (PCD) causes recurrent infections of the upper and lower respiratory tract. The disease is usually inherited as an autosomal recessive trait. To identify a gene locus for PCD, we studied a large consanguineous family of Arabic origin. Direct examination of the respiratory cilia revealed ciliary akinesia. Electron microscopic examination of cilia showed absence of the outer dynein arms. Two of four affected individuals exhibited a situs inversus, typical for Kartagener syndrome, due to randomization of the left/right body axis. A total genome scan with 340 highly polymorphic microsatellites was performed. We localized a new gene locus for PCD to a region of homozygosity by descent on chromosome 5p15-p14 with a parametric multipoint logarithm of odds ratio (LOD) score of Zmax = 3.51 flanked by markers D5S2095 and D5S502 within an interval of 20 centimorgans sex-averaged genetic distance. Applying a polymerase chain reaction-based approach, we identified a 1.5-kb partial complementary DNA of DNAH5 encoding a Chlamydomonas-related axonemal heavy dynein chain within the critical disease interval of this new PCD locus. On the basis of the Chlamydomonas model for PCD, this gene represents an excellent candidate for PCD.},
  langid = {english},
  pmid = {11062149},
  keywords = {Amino Acid Sequence,Chromosome Mapping,Chromosomes; Human; Pair 5,Ciliary Motility Disorders,Dyneins,Genetic Linkage,Genetic Markers,Homozygote,Humans,Molecular Sequence Data,Sequence Alignment}
}

@article{pan2023,
  title = {{{KMT2D}} Deficiency Drives Lung Squamous Cell Carcinoma and Hypersensitivity to {{RTK-RAS}} Inhibition},
  author = {Pan, Yuanwang and Han, Han and Hu, Hai and Wang, Hua and Song, Yueqiang and Hao, Yuan and Tong, Xinyuan and Patel, Ayushi S. and Misirlioglu, Selim and Tang, Sittinon and Huang, Hsin-Yi and Geng, Ke and Chen, Ting and Karatza, Angeliki and Sherman, Fiona and Labbe, Kristen E. and Yang, Fan and Chafitz, Alison and Peng, Chengwei and Guo, Chenchen and Moreira, Andre L. and Velcheti, Vamsidhar and Lau, Sally C. M. and Sui, Pengfei and Chen, Haiquan and Diehl, J. Alan and Rustgi, Anil K. and Bass, Adam J. and Poirier, John T. and Zhang, Xiaoyang and Ji, Hongbin and Zhang, Hua and Wong, Kwok-Kin},
  year = {2023},
  month = jan,
  journal = {Cancer Cell},
  volume = {41},
  number = {1},
  pages = {88-105.e8},
  issn = {1878-3686},
  doi = {10.1016/j.ccell.2022.11.015},
  abstract = {Lung squamous cell carcinoma (LUSC) represents a major subtype of lung cancer with limited treatment options. KMT2D is one of the most frequently mutated genes in LUSC ({$>$}20\%), and yet its role in LUSC oncogenesis remains unknown. Here, we identify KMT2D as a key regulator of LUSC tumorigenesis wherein Kmt2d deletion transforms lung basal cell organoids to LUSC. Kmt2d loss increases activation of receptor tyrosine kinases (RTKs), EGFR and ERBB2, partly through reprogramming the chromatin landscape to repress the expression of protein tyrosine phosphatases. These events provoke a robust elevation in the oncogenic RTK-RAS signaling. Combining SHP2 inhibitor SHP099 and pan-ERBB inhibitor afatinib inhibits lung tumor growth in Kmt2d-deficient LUSC murine models and in patient-derived xenografts (PDXs) harboring KMT2D mutations. Our study identifies KMT2D as a pivotal epigenetic modulator for LUSC oncogenesis and suggests that KMT2D loss renders LUSC therapeutically vulnerable to RTK-RAS inhibition.},
  langid = {english},
  pmid = {36525973},
  keywords = {Animals,Carcinogenesis,Carcinoma; Non-Small-Cell Lung,Carcinoma; Squamous Cell,Cell Transformation; Neoplastic,EGFR,ERBB2,Humans,KMT2D,Lung,Lung Neoplasms,lung squamous cell carcinoma,Mice,organoids,Protein-Tyrosine Kinases,ras Proteins,SHP2}
}

@article{park2017,
  title = {Keratinization of {{Lung Squamous Cell Carcinoma Is Associated}} with {{Poor Clinical Outcome}}},
  author = {Park, Hye Jung and Cha, Yoon-Jin and Kim, Seong Han and Kim, Arum and Kim, Eun Young and Chang, Yoon Soo},
  year = {2017},
  month = apr,
  journal = {Tuberculosis and Respiratory Diseases},
  volume = {80},
  number = {2},
  pages = {179--186},
  issn = {1738-3536},
  doi = {10.4046/trd.2017.80.2.179},
  abstract = {BACKGROUND: Although the World Health Organization (WHO) classification of lung squamous cell carcinoma (SCC) was revised in 2015, its clinical implications for lung SCC subsets remain unclear. We investigated whether the morphologic characteristics of lung SCC, including keratinization, were associated with clinical parameters and clinical outcome of patients. METHODS: A total of 81 patients who underwent curative surgical resection of diagnosed lung SCC, were enrolled in this study. Attributes such as keratinization, tumor budding, single cell invasion, and nuclear size within the tumor, as well as immunohistochemistry of Bcl-xL and pS6 expressions, were evaluated. RESULTS: The keratinizing and nonkeratinizing subtypes did not differ with respect to age, sex, TNM stage, and morphologic parameters such as nuclear diameter, tumor budding, and single cell invasion at the tumor edge. Most patients with the keratinizing subtype (98.0\%) had a history of smoking, whereas the nonkeratinizing group had a relatively higher proportion of never-smokers relative to the keratinizing group (24.0\% vs. 2.0\%; p=0.008, chi-square test). Expression of pS6 (a surrogate marker of mammalian target of rapamycin complex 1 [mTORC1] signaling that regulates keratinocyte differentiation), and Bcl-xL (a key anti-apoptotic molecule that may inhibit keratinization), did not correlate significantly with the presence of keratinization. Patients with the keratinizing subtype had a significantly shorter overall survival (85.2 months vs. 135.7 months, p=0.010, log-rank test), and a multivariate analysis showed that keratinization was an independent, poor prognostic factor (hazard ratio, 2.389; 95\% confidence interval, 1.090-5.233; p=0.030). CONCLUSION: In lung SCC, keratinization is associated with a poor prognosis, and might be associated with smoking.},
  langid = {english},
  pmcid = {PMC5392489},
  pmid = {28416958},
  keywords = {Bcl-X Protein,Carcinoma; Squamous Cell,Lung}
}

@article{picard2021,
  title = {Integration Strategies of Multi-Omics Data for Machine Learning Analysis},
  author = {Picard, M. and {Scott-Boyer}, M. P. and Bodein, A. and Perin, O. and Droit, A.},
  year = {2021},
  journal = {Comput Struct Biotechnol J},
  volume = {19},
  pages = {3735--3746},
  issn = {2001-0370 (Print) 2001-0370 (Linking)},
  doi = {10.1016/j.csbj.2021.06.030},
  abstract = {Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these data have been obtained by machine learning algorithms that have produced diagnostic and classification biomarkers. Most biomarkers obtained to date however only include one omic measurement at a time and thus do not take full advantage of recent multi-omics experiments that now capture the entire complexity of biological systems. Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. We have summarized the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications.},
  pmcid = {PMC8258788},
  keywords = {Deep learning,Integration strategy,Machine learning,Multi-omics,Multi-view,Network,personal relationships that could have appeared to influence the work reported in,this paper.}
}

@article{pierre-jean2020,
  title = {Clustering and Variable Selection Evaluation of 13 Unsupervised Methods for Multi-Omics Data Integration},
  author = {{Pierre-Jean}, Morgane and Deleuze, Jean-Fran{\c c}ois and Le Floch, Edith and Mauger, Florence},
  year = {2020},
  month = dec,
  journal = {Briefings in Bioinformatics},
  volume = {21},
  number = {6},
  pages = {2011--2030},
  issn = {1477-4054},
  doi = {10.1093/bib/bbz138},
  abstract = {Recent advances in NGS sequencing, microarrays and mass spectrometry for omics data production have enabled the generation and collection of different modalities of high-dimensional molecular data. The integration of multiple omics datasets is a statistical challenge, due to the limited number of individuals, the high number of variables and the heterogeneity of the datasets to integrate. Recently, a lot of tools have been developed to solve the problem of integrating omics data including canonical correlation analysis, matrix factorization and SM. These commonly used techniques aim to analyze simultaneously two or more types of omics. In this article, we compare a panel of 13 unsupervised methods based on these different approaches to integrate various types of multi-omics datasets: iClusterPlus, regularized generalized canonical correlation analysis, sparse generalized canonical correlation analysis, multiple co-inertia analysis (MCIA), integrative-NMF (intNMF), SNF, MoCluster, mixKernel, CIMLR, LRAcluster, ConsensusClustering, PINSPlus and multi-omics factor analysis (MOFA). We evaluate the ability of the methods to recover the subgroups and the variables that drive the clustering on eight benchmarks of simulation. MOFA does not provide any results on these benchmarks. For clustering, SNF, MoCluster, CIMLR, LRAcluster, ConsensusClustering and intNMF provide the best results. For variable selection, MoCluster outperforms the others. However, the performance of the methods seems to depend on the heterogeneity of the datasets (especially for MCIA, intNMF and iClusterPlus). Finally, we apply the methods on three real studies with heterogeneous data and various phenotypes. We conclude that MoCluster is the best method to analyze these omics data. Availability: An R package named CrIMMix is available on GitHub at https://github.com/CNRGH/crimmix to reproduce all the results of this article.},
  langid = {english},
  pmid = {31792509},
  keywords = {Algorithms,benchmarks,Cluster Analysis,Computational Biology,Computer Simulation,Genomics,Humans,multi-omics,Multivariate Analysis,Neoplasms,performance evaluation,real data,unsupervised integrative methods}
}

@article{polychrome,
  title = {Polychrome: {{Creating}} and {{Assessing Qualitative Palettes With Many Colors}}},
  author = {Coombes, Kevin and Brock, Guy and Abrams, Zachary B. and Abruzzo, Lynne V.},
  year = {2018},
  journal = {BioRxiv},
  pages = {303883}
}

@article{poudel2022,
  title = {Machine {{Learning}} for {{Prediction}} of {{Cognitive Health}} in {{Adults Using Sociodemographic}}, {{Neighbourhood Environmental}}, and {{Lifestyle Factors}}},
  author = {Poudel, G. R. and Barnett, A. and Akram, M. and Martino, E. and Knibbs, L. D. and Anstey, K. J. and Shaw, J. E. and Cerin, E.},
  year = {2022},
  month = sep,
  journal = {Int J Environ Res Public Health},
  volume = {19},
  number = {17},
  issn = {1660-4601 (Electronic) 1660-4601 (Linking)},
  doi = {10.3390/ijerph191710977},
  abstract = {The environment we live in, and our lifestyle within this environment, can shape our cognitive health. We investigated whether sociodemographic, neighbourhood environment, and lifestyle variables can be used to predict cognitive health status in adults. Cross-sectional data from the AusDiab3 study, an Australian cohort study of adults (34-97 years) (n = 4141) was used. Cognitive function was measured using processing speed and memory tests, which were categorized into distinct classes using latent profile analysis. Sociodemographic variables, measures of the built and natural environment estimated using geographic information system data, and physical activity and sedentary behaviours were used as predictors. Machine learning was performed using gradient boosting machine, support vector machine, artificial neural network, and linear models. Sociodemographic variables predicted processing speed (r(2) = 0.43) and memory (r(2) = 0.20) with good accuracy. Lifestyle factors also accurately predicted processing speed (r(2) = 0.29) but weakly predicted memory (r(2) = 0.10). Neighbourhood and built environment factors were weak predictors of cognitive function. Sociodemographic (AUC = 0.84) and lifestyle (AUC = 0.78) factors also accurately classified cognitive classes. Sociodemographic and lifestyle variables can predict cognitive function in adults. Machine learning tools are useful for population-level assessment of cognitive health status via readily available and easy-to-collect data.},
  pmcid = {PMC9517821},
  keywords = {*Residence Characteristics,*Sedentary Behavior,Adult,Australia,built environment,Cognition,Cohort Studies,Cross-Sectional Studies,Humans,Life Style,Machine Learning,memory,neighbourhood environment,physical activity,prediction,processing speed,sedentary behaviour,sociodemographic}
}

@book{Rbook,
  title = {R: {{A Language}} and {{Environment}} for {{Statistical Computing}}},
  author = {{R Core Team}},
  year = {2022},
  publisher = {{R Foundation for Statistical Computing}},
  address = {{Vienna, Austria}}
}

@article{reel2021,
  title = {Using Machine Learning Approaches for Multi-Omics Data Analysis: {{A}} Review},
  author = {Reel, P. S. and Reel, S. and Pearson, E. and Trucco, E. and Jefferson, E.},
  year = {2021},
  month = jul,
  journal = {Biotechnol Adv},
  volume = {49},
  pages = {107739},
  issn = {1873-1899 (Electronic) 0734-9750 (Linking)},
  doi = {10.1016/j.biotechadv.2021.107739},
  abstract = {With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivery of precision medicine. This review paper explores different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies.},
  keywords = {*Machine Learning,*Multi-omics,*Predictive Modelling,*Supervised Learning,*Systems Biology,*Unsupervised Learning,Algorithms,Humans,Metabolomics,Proteomics}
}

@article{shiga2015,
  title = {Cancer-{{Associated Fibroblasts}}: {{Their Characteristics}} and {{Their Roles}} in {{Tumor Growth}}},
  shorttitle = {Cancer-{{Associated Fibroblasts}}},
  author = {Shiga, Kazuyoshi and Hara, Masayasu and Nagasaki, Takaya and Sato, Takafumi and Takahashi, Hiroki and Takeyama, Hiromitsu},
  year = {2015},
  month = dec,
  journal = {Cancers},
  volume = {7},
  number = {4},
  pages = {2443--2458},
  issn = {2072-6694},
  doi = {10.3390/cancers7040902},
  abstract = {Cancer tissues are composed of cancer cells and the surrounding stromal cells (e.g., fibroblasts, vascular endothelial cells, and immune cells), in addition to the extracellular matrix. Most studies investigating carcinogenesis and the progression, invasion, metastasis, and angiogenesis of cancer have focused on alterations in cancer cells, including genetic and epigenetic changes. Recently, interactions between cancer cells and the stroma have attracted considerable attention, and increasing evidence has accumulated on this. Several researchers have gradually clarified the origins, features, and roles of cancer-associated fibroblasts (CAFs), a major component of the cancer stroma. CAFs function in a similar manner to myofibroblasts during wound healing. We previously reported the relationship between CAFs and angiogenesis. Interleukin-6 (IL-6), a multifunctional cytokine, plays a central role in regulating inflammatory and immune responses, and important roles in the progression, including proliferation, migration, and angiogenesis, of several cancers. We showed that CAFs are an important IL-6 source and that anti-IL-6 receptor antibody suppressed angiogenesis and inhibited tumor-stroma interactions. Furthermore, CAFs contribute to drug-resistance acquisition in cancer cells. The interaction between cancer cells and the stroma could be a potential target for anti-cancer therapy.},
  langid = {english},
  pmcid = {PMC4695902},
  pmid = {26690480},
  keywords = {angiogenesis,cancer-associated fibroblasts,drug resistance,interleukin 6,origin,tumor-stroma interaction}
}

@article{simon2003,
  title = {Experimental Design of {{DNA}} Microarray Experiments},
  author = {Simon, R. M. and Dobbin, K.},
  year = {2003},
  month = mar,
  journal = {Biotechniques},
  volume = {Suppl},
  pages = {16--21},
  issn = {0736-6205 (Print) 0736-6205 (Linking)},
  keywords = {*Research Design,Animals,Clinical Trials as Topic/instrumentation/methods/standards,DNA; Neoplasm/classification/genetics,DNA/*classification/*genetics,Humans,Oligonucleotide Array Sequence Analysis/*instrumentation/*methods/standards,Quality Control,Reproducibility of Results,Research/instrumentation/standards,Sample Size,Sensitivity and Specificity}
}

@article{subramanian2020,
  title = {Multi-Omics {{Data Integration}}, {{Interpretation}}, and {{Its Application}}},
  author = {Subramanian, I. and Verma, S. and Kumar, S. and Jere, A. and Anamika, K.},
  year = {2020},
  journal = {Bioinform Biol Insights},
  volume = {14},
  pages = {1177932219899051},
  issn = {1177-9322 (Print) 1177-9322 (Linking)},
  doi = {10.1177/1177932219899051},
  abstract = {To study complex biological processes holistically, it is imperative to take an integrative approach that combines multi-omics data to highlight the interrelationships of the involved biomolecules and their functions. With the advent of high-throughput techniques and availability of multi-omics data generated from a large set of samples, several promising tools and methods have been developed for data integration and interpretation. In this review, we collected the tools and methods that adopt integrative approach to analyze multiple omics data and summarized their ability to address applications such as disease subtyping, biomarker prediction, and deriving insights into the data. We provide the methodology, use-cases, and limitations of these tools; brief account of multi-omics data repositories and visualization portals; and challenges associated with multi-omics data integration.},
  pmcid = {PMC7003173},
  keywords = {biomarker prediction,conflicts of interest with respect to the research; authorship; and/or,data integration,data repositories,disease subtyping,multi-omics,publication of this article.}
}

@article{sutton2005,
  title = {Classification and {{Regression Trees}}, {{Bagging}}, and {{Boosting}}},
  author = {Sutton, Clifton D.},
  year = {2005},
  journal = {Handbook of Statistics},
  volume = {24},
  pages = {303--329},
  publisher = {{Elsevier}},
  doi = {10.1016/S0169-7161(04)24011-1},
  abstract = {Semantic Scholar extracted view of "Classification and Regression Trees, Bagging, and Boosting" by C. Sutton},
  isbn = {9780444511416},
  langid = {english}
}

@article{tan2011,
  title = {A 5-{{MicroRNA Signature}} for {{Lung Squamous Cell Carcinoma Diagnosis}} and Hsa-{{miR-31}} for {{Prognosis}}},
  author = {Tan, Xiaogang and Qin, Wenyan and Zhang, Liang and Hang, Jie and Li, Baozhong and Zhang, Cuiyan and Wan, Junting and Zhou, Fang and Shao, Kang and Sun, Yimin and Wu, Jianping and Zhang, Xun and Qiu, Bin and Li, Ning and Shi, Susheng and Feng, Xiaoli and Zhao, Shouhua and Wang, Zhen and Zhao, Xiaohong and Chen, Zhaoli and Mitchelson, Keith and Cheng, Jing and Guo, Yong and He, Jie},
  year = {2011},
  month = oct,
  journal = {Clinical Cancer Research},
  volume = {17},
  number = {21},
  pages = {6802--6811},
  issn = {1078-0432},
  doi = {10.1158/1078-0432.CCR-11-0419},
  abstract = {Purpose: Recent studies have suggested that microRNA biomarkers could be useful for stratifying lung cancer subtypes, but microRNA signatures varied between different populations. Squamous cell carcinoma (SCC) is one major subtype of lung cancer that urgently needs biomarkers to aid patient management. Here, we undertook the first comprehensive investigation on microRNA in Chinese SCC patients.Experimental Design: MicroRNA expression was measured in cancerous and noncancerous tissue pairs strictly collected from Chinese SCC patients (stages I\textendash III), who had not been treated with chemotherapy or radiotherapy prior to surgery. The molecular targets of proposed microRNA were further examined.Results: We identified a 5-microRNA classifier (hsa-miR-210, hsa-miR-182, hsa-miR-486-5p, hsa-miR-30a, and hsa-miR-140-3p) that could distinguish SCC from normal lung tissues. The classifier had an accuracy of 94.1\% in a training cohort (34 patients) and 96.2\% in a test cohort (26 patients). We also showed that high expression of hsa-miR-31 was associated with poor survival in these 60 SCC patients by Kaplan\textendash Meier analysis (P = 0.007), by univariate Cox analysis (P = 0.011), and by multivariate Cox analysis (P = 0.011). This association was independently validated in a separate cohort of 88 SCC patients (P = 0.008, 0.011, and 0.003 in Kaplan\textendash Meier analysis, univariate Cox analysis, and multivariate Cox analysis, respectively). We then determined that the tumor suppressor DICER1 is a target of hsa-miR-31. Expression of hsa-miR-31 in a human lung cancer cell line repressed DICER1 activity but not PPP2R2A or LATS2.Conclusions: Our results identified a new diagnostic microRNA classifier for SCC among Chinese patients and a new prognostic biomarker, hsa-miR-31. Clin Cancer Res; 17(21); 6802\textendash 11. \textcopyright 2011 AACR.}
}

@article{tcga2017,
  title = {Integrated Genomic Characterization of Oesophageal Carcinoma},
  author = {{Cancer Genome Atlas Research Network}},
  year = {2017},
  month = jan,
  journal = {Nature},
  volume = {541},
  number = {7636},
  pages = {169--175},
  issn = {1476-4687 (Electronic) 0028-0836 (Linking)},
  doi = {10.1038/nature20805},
  abstract = {Oesophageal cancers are prominent worldwide; however, there are few targeted therapies and survival rates for these cancers remain dismal. Here we performed a comprehensive molecular analysis of 164 carcinomas of the oesophagus derived from Western and Eastern populations. Beyond known histopathological and epidemiologic distinctions, molecular features differentiated oesophageal squamous cell carcinomas from oesophageal adenocarcinomas. Oesophageal squamous cell carcinomas resembled squamous carcinomas of other organs more than they did oesophageal adenocarcinomas. Our analyses identified three molecular subclasses of oesophageal squamous cell carcinomas, but none showed evidence for an aetiological role of human papillomavirus. Squamous cell carcinomas showed frequent genomic amplifications of CCND1 and SOX2 and/or TP63, whereas ERBB2, VEGFA and GATA4 and GATA6 were more commonly amplified in adenocarcinomas. Oesophageal adenocarcinomas strongly resembled the chromosomally unstable variant of gastric adenocarcinoma, suggesting that these cancers could be considered a single disease entity. However, some molecular features, including DNA hypermethylation, occurred disproportionally in oesophageal adenocarcinomas. These data provide a framework to facilitate more rational categorization of these tumours and a foundation for new therapies.},
  pmcid = {PMC5651175},
  keywords = {*Genomics,Adenocarcinoma/classification/*genetics,Carcinoma; Squamous Cell/classification/*genetics,Esophageal Neoplasms/*classification/*genetics,Esophageal Squamous Cell Carcinoma,Genome; Human/*genetics,Humans,Molecular Targeted Therapy/trends,Mutation,Stomach Neoplasms/classification/genetics}
}

@book{therneau2000,
  title = {Modeling {{Survival Data}}: {{Extending}} the {{Cox Model}}},
  shorttitle = {Modeling {{Survival Data}}},
  author = {Therneau, Terry M. and Grambsch, Patricia M.},
  editor = {Dietz, K. and Gail, M. and Krickeberg, K. and Samet, J. and Tsiatis, A.},
  year = {2000},
  series = {Statistics for {{Biology}} and {{Health}}},
  publisher = {{Springer}},
  address = {{New York, NY}},
  doi = {10.1007/978-1-4757-3294-8},
  isbn = {978-1-4419-3161-0 978-1-4757-3294-8},
  keywords = {Cox Model,Cox Regression Model,Estimator,Radiologieinformationssystem,SAS,statistics,Survival analysis,Survival Data}
}

@article{tilley2015,
  title = {Cilia Dysfunction in Lung Disease},
  author = {Tilley, Ann E. and Walters, Matthew S. and Shaykhiev, Renat and Crystal, Ronald G.},
  year = {2015},
  journal = {Annual Review of Physiology},
  volume = {77},
  pages = {379--406},
  issn = {1545-1585},
  doi = {10.1146/annurev-physiol-021014-071931},
  abstract = {A characteristic feature of the human airway epithelium is the presence of ciliated cells bearing motile cilia, specialized cell surface projections containing axonemes composed of microtubules and dynein arms, which provide ATP-driven motility. In the airways, cilia function in concert with airway mucus to mediate the critical function of mucociliary clearance, cleansing the airways of inhaled particles and pathogens. The prototypical disorder of respiratory cilia is primary ciliary dyskinesia, an inherited disorder that leads to impaired mucociliary clearance, to repeated chest infections, and to the progressive destruction of lung architecture. Numerous acquired lung diseases are also marked by abnormalities in both cilia structure and function. In this review we summarize current knowledge regarding airway ciliated cells and cilia, how they function to maintain a healthy epithelium, and how disorders of cilia structure and function contribute to inherited and acquired lung disease.},
  langid = {english},
  pmcid = {PMC4465242},
  pmid = {25386990},
  keywords = {airway,Animals,Axoneme,cilia,Cilia,Ciliary Motility Disorders,Disease Models; Animal,epithelium,Humans,Lung Diseases,Mice,Microtubules,Mucociliary Clearance,mucociliary escalator,Respiratory Mucosa}
}

@article{vlachavas2021,
  title = {A {{Detailed Catalogue}} of {{Multi-Omics Methodologies}} for {{Identification}} of {{Putative Biomarkers}} and {{Causal Molecular Networks}} in {{Translational Cancer Research}}},
  author = {Vlachavas, E. I. and Bohn, J. and Uckert, F. and Nurnberg, S.},
  year = {2021},
  month = mar,
  journal = {Int J Mol Sci},
  volume = {22},
  number = {6},
  issn = {1422-0067 (Electronic) 1422-0067 (Linking)},
  doi = {10.3390/ijms22062822},
  abstract = {Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.},
  pmcid = {PMC8000236},
  keywords = {*Biomarkers; Tumor/genetics/metabolism,*Computational Biology,*Genomics,*Metabolomics,*Neoplasms/genetics/metabolism/therapy,*Proteomics,*Translational Research; Biomedical,analysis tools,Biomarkers; Tumor,Computational Biology,Genomics,Humans,integrative methods,literature review,Metabolomics,multi-omics data integration,Neoplasms,oncology,personalized medicine,Proteomics,supervised data integration,translational cancer research,Translational Research; Biomedical,unsupervised data integration}
}

@article{zhang2019,
  title = {{{MicroRNA-34}} Family: A Potential Tumor Suppressor and Therapeutic Candidate in Cancer},
  shorttitle = {{{MicroRNA-34}} Family},
  author = {Zhang, Lu and Liao, Yi and Tang, Liling},
  year = {2019},
  month = feb,
  journal = {Journal of Experimental \& Clinical Cancer Research},
  volume = {38},
  number = {1},
  pages = {53},
  issn = {1756-9966},
  doi = {10.1186/s13046-019-1059-5},
  abstract = {MicroRNA-34 (miR-34) has been reported to be dysregulated in various human cancers and regarded as a tumor suppressive microRNA because of its synergistic effect with the well-known tumor suppressor p53. Along with the application of MRX34, the first tumor-targeted microRNA drug which based on miR-34a mimics, on phase I clinical trial (NCT01829971), the significance of miR-34 is increasingly recognized. miR-34 plays a crucial role on repressing tumor progression by involving in epithelial-mesenchymal transition (EMT) via EMT- transcription factors, p53 and some important signal pathways. Not only that, numerous preclinical researches revealed the giant potential of miR-34a on cancer therapy through diversiform nano-scaled delivery systems. Here, we provide an overview about the function of miR-34 in various cancers and the mechanism of miR-34 in tumor-associated EMT. Furthermore, its potential role as a microRNA therapeutic candidate is also discussed. Notwithstanding some obstacles existed, the extensive application prospect of miR-34 on oncotherapy cannot be neglected.},
  keywords = {Dysregulation,EMT,miR-34,Oncotherapy,Tumor-suppressive}
}
