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Cited by 696Bioinformatics and Genomic NetworksGene expression and cancer classificationMachine Learning in Bioinformatics

Integration strategies of multi-omics data for machine learning analysis

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Picard, Milan, Scott-Boyer, Marie-Pier, Bodein, Antoine, Périn, Olivier, Droit, Arnaud·2021·Computational and Structural Biotechnology Journal
DOI 10.1016/j.csbj.2021.06.030PMC PMC8258788
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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.

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Crossref · 10.1016/j.csbj.2021.06.030

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  • Models often show high performance on training datasets but fail to generalize to independent cohorts, a pattern frequently reported in Alzheimer's disease multi-omics studies.

    “Models often show high performance on training datasets but fail to generalize to independent cohorts, a pattern frequently reported in AD multi-omics studies410,411.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [410]

    The Challenges section (page unavailable) says multi-omics models can overfit high-dimensional datasets, reducing their generalizability on new data. The Summary and outlook likewise says deep-learning models often overfit. The source does not establish that models perform well in training but fail in independent cohorts, or that this pattern is frequently reported specifically in Alzheimer’s disease multi-omics studies. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

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  1. [2]Ahmed Z. Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis Hum. Genomics 14 2020 PMC7530549 33008459
  2. [3]Burney I.A. Lakhtakia R. Precision Medicine: Where have we reached and where are we headed? Sultan Qaboos Univ. Med. J. 17 2017 PMC5642353 29062546
  3. [4]Jaccard E. Cornuz J. Waeber G. Guessous I. Evidence-based precision medicine is needed to move toward general internal precision medicine J Gen Intern Med 33 2018 PMC5756155 28808923
  4. [5]Tebani A. Afonso C. Marret S. Bekri S. Omics-based strategies in precision medicine: toward a paradigm shift in inborn errors of metabolism investigations Int J Mol Sci 17 2016 PMC5037827 27649151
  5. [6]Menyhárt O. Győrffy B. Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis Comput Struct Biotechnol J 19 2021 949 960 33613862 PMC7868685
  6. [9]Dahal S. Yurkovich J.T. Xu H. Palsson B.O. Yang L. Synthesizing systems biology knowledge from omics using genome-scale models Proteomics 20 2020 e1900282 PMC7501203 32579720
  7. [10]Yan J. Risacher S.L. Shen L. Saykin A.J. Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data Brief. Bioinform. 19 2018 1370 1381 28679163 PMC6454489
  8. [11]Zarayeneh N. Integration of multi-omics data for integrative gene regulatory network inference Int. J. Data Mining Bioinformatics 18 2017 223 PMC5771269 29354189
  9. [13]Sharifi-Noghabi H. Zolotareva O. Collins C.C. Ester M. MOLI: multi-omics late integration with deep neural networks for drug response prediction Bioinformatics 35 2019 i501 i509 31510700 PMC6612815
  10. [14]Tini G. Marchetti L. Priami C. Scott-Boyer M.-P. Multi-omics integration—a comparison of unsupervised clustering methodologies Briefings Bioinf. 20 2019 1269 1279 29272335
  11. [16]Higdon R. The promise of multi-omics and clinical data integration to identify and target personalized healthcare approaches in autism spectrum disorders OMICS 19 2015 197 208 25831060 PMC4389910
  12. [17]Zitnik M. Machine learning for integrating data in biology and medicine: principles, practice, and opportunities Inf. Fusion 50 2019 71 91 30467459 PMC6242341
  13. [18]Bersanelli M. Methods for the integration of multi-omics data: mathematical aspects BMC Bioinf 17 Suppl 2 2016 15 PMC4959355 26821531
  14. [19]Huang S. Chaudhary K. Garmire L.X. More is better: recent progress in multi-omics data integration methods Front Genet 8 2017 84 28670325 PMC5472696
  15. [20]Li Y. Wu F.-X. Ngom A. A review on machine learning principles for multi-view biological data integration Brief. Bioinform. 19 2018 325 340 28011753
  16. [21]Nicora G. Vitali F. Dagliati A. Geifman N. Bellazzi R. Integrated multi-omics analyses in oncology: a review of machine learning methods and tools Front Oncol 10 2020 1030 32695678 PMC7338582
  17. [23]Wu C. A selective review of multi-level omics data integration using variable selection High Throughput 8 2019 PMC6473252 30669303
  18. [24]Meng C. Dimension reduction techniques for the integrative analysis of multi-omics data Brief. Bioinform. 17 2016 628 641 26969681 PMC4945831
  19. [25]Franco E.F. Performance comparison of deep learning autoencoders for cancer subtype detection using multi-omics data Cancers 13 2021 PMC8122584 33921978
  20. [26]Rappoport N. Shamir R. Multi-omic and multi-view clustering algorithms: review and cancer benchmark Nucl Acids Res 46 2018 10546 10562 30295871 PMC6237755
  21. [27]Chauvel C. Novoloaca A. Veyre P. Reynier F. Becker J. Evaluation of integrative clustering methods for the analysis of multi-omics data Brief. Bioinform. 21 2020 541 552 31220206
  22. [28]Wani N, Raza K. Integrative Approaches to Reconstruct Regulatory Networks From Multi-Omics Data: A Review of State-of-the-Art Methods. doi:10.20944/preprints201804.0352.v1. 31499298 · DOI
  23. [29]Lee B. Zhang S. Poleksic A. Xie L. Heterogeneous multi-layered network model for omics data integration and analysis Front Genet 10 2019 1381 32063919 PMC6997577
  24. [30]Cavill R. Jennen D. Kleinjans J. Briedé J.J. Transcriptomic and metabolomic data integration Brief. Bioinform. 17 2016 891 901 26467821
  25. [31]Eicher T. Metabolomics and multi-omics integration: a survey of computational methods and resources Metabolites 10 2020 PMC7281435 32429287
  26. [32]Mantini G. Pham T.V. Piersma S.R. Jimenez C.R. Computational analysis of phosphoproteomics data in multi-omics cancer studies Proteomics 21 2021 e1900312 32875713
  27. [33]Canzler S. Prospects and challenges of multi-omics data integration in toxicology Arch Toxicol 94 2020 371 388 32034435
  28. [34]Culibrk L. Croft C.A. Tebbutt S.J. Systems biology approaches for host-fungal interactions: an expanding multi-omics frontier OMICS 20 2016 127 138 26885725 PMC4799697
  29. [35]Khan M.M. Multi-omics strategies uncover host-pathogen interactions ACS Infect Dis 5 2019 493 505 30857388 PMC13488112
  30. [36]Jamil I.N. Systematic multi-omics integration (MOI) approach in plant systems biology Front Plant Sci 11 2020 944 32754171 PMC7371031
  31. [37]Leon-Mimila P. Wang J. Huertas-Vazquez A. Relevance of multi-omics studies in cardiovascular diseases Front Cardiovasc Med 6 2019 91 31380393 PMC6656333
  32. [38]Labory J. Multi-omics approaches to improve mitochondrial disease diagnosis: challenges, advances, and perspectives Front Mol Biosci 7 2020 590842 PMC7667268 33240932
  33. [39]Morello G. Salomone S. D’Agata V. Conforti F.L. Cavallaro S. From multi-omics approaches to precision medicine in amyotrophic lateral sclerosis Front Neurosci 14 2020 577755 PMC7661549 33192262
  34. [42]Domingos P. A few useful things to know about machine learning Commun ACM 55 2012 78 87
  35. [43]Crick F. Central dogma of molecular biology Nature 227 1970 561 563 4913914
  36. [45]Hall MA. Correlation-based feature selection for machine learning. http://www.cs.waikato.ac.nz/~ml/publications/1999/99MH-Thesis.pdf.
  37. [47]Kononenko I. Estimating attributes: Analysis and extensions of RELIEF. in Machine Learning: ECML-94 171–182 (Springer Berlin Heidelberg, 1994).
  38. [49]Peng H. Long F. Ding C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy IEEE Trans Pattern Anal Mach Intell 27 2005 1226 1238 16119262
  39. [50]Scornet E. Trees, forests, and impurity-based variable importance. arXiv [math.ST] (2020).
  40. [51]Vinga S. Structured sparsity regularization for analyzing high-dimensional omics data Brief. Bioinform. 22 2021 77 87 32597465
  41. [56]Beh E.J. Simple correspondence analysis: a bibliographic review Int. Stat. Rev. 72 2007 257 284
  42. [57]Sompairac N. Independent component analysis for unraveling the complexity of cancer omics datasets Int J Mol Sci 20 2019 PMC6771121 31500324
  43. [60]Peharz R. Pernkopf F. Sparse nonnegative matrix factorization with ℓ0-constraints Neurocomputing 80 2012 38 46 22505792 PMC3312776
  44. [61]Liu R, Niang N, Saporta G, Wang H. Sparse Correspondence Analysis for Contingency Tables. arXiv [stat.ME] (2020).
  45. [62]Park M. Kim D. Moon K. Park T. integrative analysis of multi-omics data based on blockwise sparse principal components Int J Mol Sci 21 2020 PMC7663540 33147797
  46. [63]De Tayrac M. Lê S. Aubry M. Mosser J. Husson F. Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: Multiple Factor Analysis approach BMC Genomics 10 2009 1 17 19154582 PMC2636827
  47. [64]Tang TM, Allen GI. Integrated Principal Components Analysis. arXiv [stat.ME] (2018).
  48. [65]EL-Manzalawy Y, Hsieh T-Y, Shivakumar M, Kim D, Honavar V. Min-Redundancy and Max-Relevance Multi-view Feature Selection for Predicting Ovarian Cancer Survival using Multi-omics Data. doi:10.1101/317982. PMC6157248 30255801 · DOI
  49. [66]Spicker J.S. Brunak S. Frederiksen K.S. Toft H. Integration of clinical chemistry, expression, and metabolite data leads to better toxicological class separation Toxicol Sci 102 2008 444 454 18178960
  50. [67]Abdi H. Williams L.J. Valentin D. Multiple factor analysis: principal component analysis for multitable and multiblock data sets: Multiple factor analysis Wiley Interdiscip Rev Comput Stat 5 2013 149 179
  51. [68]Grossi E. Buscema M. Introduction to artificial neural networks Eur J Gastroenterol Hepatol 19 2007 1046 1054 17998827
  52. [69]Xie G. Group lasso regularized deep learning for cancer prognosis from multi-omics and clinical features Genes 10 2019 PMC6471789 30901858
  53. [71]Talukder A. Barham C. Li X. Hu H. Interpretation of deep learning in genomics and epigenomics Briefings Bioinf 2020 10.1093/bib/bbaa177 PMC8138893 34020542 · DOI · Paper page
  54. [72]Martorell-Marugán J. et al. Deep Learning in Omics Data Analysis and Precision Medicine. in Computational Biology (ed. Husi, H.) (Codon Publications, 2019). 31815397
  55. [73]Deng L. Pathway-guided deep neural network toward interpretable and predictive modeling of drug sensitivity J Chem Inf Model 60 2020 4497 4505 32804489
  56. [75]Lee J.D. Hastie T.J. Learning the structure of mixed graphical models J Comput Graph Stat 24 2015 230 253 26085782 PMC4465824
  57. [76]Altenbuchinger M. Weihs A. Quackenbush J. Grabe H.J. Zacharias H.U. Gaussian and Mixed Graphical Models as (multi-)omics data analysis tools Biochim Biophys Acta (BBA) – Gene Regulatory Mech 1863 2020 194418 PMC7166149 31639475
  58. [78]Manatakis D.V. Raghu V.K. Benos P.V. piMGM: incorporating multi-source priors in mixed graphical models for learning disease networks Bioinformatics 34 2018 i848 i856 30423087 PMC6129280
  59. [79]Petralia F. Wang P. Yang J. Tu Z. Integrative random forest for gene regulatory network inference Bioinformatics 31 2015 i197 i205 26072483 PMC4542785
  60. [80]Zhong W. Inferring regulatory networks from mixed observational data using directed acyclic graphs Front Genet 11 2020 PMC7038820 32127796
  61. [81]Hawe J.S. Theis F.J. Heinig M. Inferring interaction networks from multi-omics data Front Genet 10 2019 PMC6582773 31249591
  62. [82]Lanckriet GRG. Learning the kernel matrix with semideønite programming. https://www.jmlr.org/papers/volume5/lanckriet04a/lanckriet04a.pdf (2004).
  63. [83]Joachims T. Support Vector Machines. Learning to Classify Text Using Support Vector Machines 35–44 (2002) doi: 10.1007/978-1-4615-0907-3_3. · DOI
  64. [84]Yang H. Cao H. He T. Wang T. Cui Y. Multilevel heterogeneous omics data integration with kernel fusion Briefings Bioinf 2018 10.1093/bib/bby115 30496340 · DOI
  65. [85]Zhu B. Integrating Clinical and Multiple Omics Data for Prognostic Assessment across Human Cancers Sci Rep 7 2017 PMC5717223 29209073
  66. [87]Wilson CM, Li K, Kuan P-F, Wang X. Multiple-kernel learning for genomic data mining and prediction. doi: 10.1101/415950. PMC6694479 31416413 · DOI
  67. [90]Tao M. Classifying breast cancer subtypes using multiple kernel learning based on omics data Genes 10 2019 PMC6471546 30866472
  68. [91]Zhang Y. Li A. Peng C. Wang M. Improve glioblastoma multiforme prognosis prediction by using feature selection and multiple kernel learning IEEE/ACM Trans Comput Biol Bioinf 13 2016 825 835 27071189
  69. [92]He Z. Zhang J. Yuan X. Zhang Y. Integrating somatic mutations for breast cancer survival prediction using machine learning methods Front Genet 11 2021 PMC7848170 33537063
  70. [93]Wang B. Zhu J. Pierson E. Ramazzotti D. Batzoglou S. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning Nat Methods 14 2017 414 416 28263960
  71. [94]Ramazzotti D. Lal A. Wang B. Batzoglou S. Sidow A. Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival Nat Commun 9 2018 PMC6203719 30367051
  72. [95]Mariette J, Villa-Vialaneix N. Unsupervised multiple kernel learning for heterogeneous data integration. doi: 10.1101/139287. 29077792 · DOI
  73. [96]Speicher N.K. Pfeifer N. Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery Bioinformatics 31 2015 i268 i275 26072491 PMC4765854
  74. [97]Paul D, Chakraborty S, Das S, Xu J. Kernel k-Means, By All Means: Algorithms and Strong Consistency. arXiv [stat.ML] (2020).
  75. [98]Wang B. Similarity network fusion for aggregating data types on a genomic scale Nat Methods 11 2014 333 337 24464287
  76. [99]Ma T. Zhang A. Affinity network fusion and semi-supervised learning for cancer patient clustering Methods 145 2018 16 24 29807109
  77. [100]Wen Y. Multi-dimensional data integration algorithm based on random walk with restart BMC Bioinf 22 2021 PMC7912853 33639858
  78. [101]Jarada T, Rokne J, Alhajj R. SNF-NN: Computational Method To Predict Drug-Disease Interactions Using Similarity Network Fusion and Neural Networks. doi:10.21203/rs.3.rs-56433/v1. PMC7821180 33482713 · DOI
  79. [102]Chierici M. Integrative network fusion: a multi-omics approach in molecular profiling Front Oncol 10 2020 1065 32714870 PMC7340129
  80. [103]Valdeolivas A. Random walk with restart on multiplex and heterogeneous biological networks Bioinformatics 35 2019 497 505 30020411
  81. [104]Shang H. Liu Z.-P. Network-based prioritization of cancer genes by integrative ranks from multi-omics data Comput Biol Med 119 2020 103692 32339126
  82. [106]Liu W. Topologically inferring risk-active pathways toward precise cancer classification by directed random walk Bioinformatics 29 2013 2169 2177 23842813
  83. [107]Kim S.Y. Jeong H.-H. Kim J. Moon J.-H. Sohn K.-A. Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies Biol Direct 14 2019 PMC6489180 31036036
  84. [108]Kim SY, Choe EK, Shivakumar M, Kim D, Sohn K-A. Multi-layered network-based pathway activity inference using directed random walks: application to predicting clinical outcomes in urologic cancer. doi: 10.1101/2020.07.22.163949. PMC8388033 33543748 · DOI
  85. [109]Nelson W. To embed or not: network embedding as a paradigm in computational biology Front Genet 10 2019 PMC6504708 31118945
  86. [110]Yue X. Graph embedding on biomedical networks: methods, applications and evaluations Bioinformatics 2019 10.1093/bioinformatics/btz718 PMC7703771 31584634 · DOI
  87. [111]Luo Y. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information Nat Commun 8 2017 PMC5603535 28924171
  88. [112]Cho H. Berger B. Peng J. Diffusion component analysis: unraveling functional topology in biological networks Res. Comput. Mol. Biol. 9029 2015 62 64 28748230 PMC5524124
  89. [114]Gligorijević V. Barot M. Bonneau R. deepNF: deep network fusion for protein function prediction Bioinformatics 34 2018 3873 3881 29868758 PMC6223364
  90. [115]Zeng X. deepDR: a network-based deep learning approach to in silico drug repositioning Bioinformatics 35 2019 5191 5198 31116390 PMC6954645
  91. [116]Chen Y, de Rijke M. A Collective Variational Autoencoder for Top-N Recommendation with Side Information. in Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems 3–9 (Association for Computing Machinery, 2018).
  92. [117]Manica M. Cadow J. Mathis R. Martínez M.R. PIMKL: Pathway-induced multiple kernel learning npj Syst Biol Appl 5 2019 PMC6401099 30854223
  93. [118]Tepeli Y.I. Ünal A.B. Akdemir F.M. Tastan O. PAMOGK: a pathway graph kernel-based multiomics approach for patient clustering Bioinformatics 36 2021 5237 5246 32730565
  94. [119]Wu Z. A Comprehensive survey on graph neural networks IEEE Trans Neural Networks Learn Syst 32 2021 4 24 32217482
  95. [120]Wang Z. Zhou M. Arnold C. Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing Bioinformatics 36 2020 i525 i533 32657387 PMC7355266
  96. [121]Singha, M. et al. GraphGR: A graph neural network to predict the effect of pharmacotherapy on the cancer cell growth. doi: 10.1101/2020.05.20.107458. · DOI
  97. [122]Zitnik M. Agrawal M. Leskovec J. Modeling polypharmacy side effects with graph convolutional networks Bioinformatics 34 2018 i457 i466 29949996 PMC6022705
  98. [123]Liu Q, Hu Z, Jiang R, Zhou M. DeepCDR: a hybrid graph convolutional network for predicting cancer drug response. doi:10.1101/2020.07.08.192930. 33381841 · DOI
  99. [124]Muzio G. O’Bray L. Borgwardt K. Biological network analysis with deep learning Briefings Bioinf 22 2021 1515 1530 PMC7986589 33169146
  100. [125]Zhang L. Deep learning-based multi-omics data integration reveals two prognostic subtypes in high-risk neuroblastoma Front Genet 9 2018 477 30405689 PMC6201709
  101. [126]Xu J. A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data BMC Bioinf 20 2019 PMC6819613 31660856
  102. [127]Lin Y. Zhang W. Cao H. Li G. Du W. Classifying breast cancer subtypes using deep neural networks based on multi-omics data Genes 11 2020 888 PMC7464481 32759821
  103. [128]Islam M.M. An integrative deep learning framework for classifying molecular subtypes of breast cancer Comput Struct Biotechnol J 18 2020 2185 2199 32952934 PMC7473884
  104. [129]Yang H. Chen R. Li D. Wang Z. Subtype-GAN: a deep learning approach for integrative cancer subtyping of multi-omics data Bioinformatics 2021 10.1093/bioinformatics/btab109 33599254 · DOI
  105. [130]Gaynanova I. Li G. Structural learning and integrative decomposition of multi-view data Biometrics 75 2019 1121 1132 31254385
  106. [131]Lee D.D. Seung H.S. Learning the parts of objects by non-negative matrix factorization Nature 401 1999 788 791 10548103
  107. [132]Zhang S. Discovery of multi-dimensional modules by integrative analysis of cancer genomic data Nucl Acids Res 40 2012 9379 9391 22879375 PMC3479191
  108. [133]Yang Z. Michailidis G. A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data Bioinformatics btv544 2015 10.1093/bioinformatics/btv544 PMC5006236 26377073 · DOI · Paper page
  109. [134]Chalise P. Fridley B.L. Integrative clustering of multi-level ‘omic data based on non-negative matrix factorization algorithm PLoS ONE 12 2017 e0176278 PMC5411077 28459819
  110. [135]Cantini L. Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer Nat Commun 12 2021 PMC7785750 33402734
  111. [136]Luo Y. Tao D. Ramamohanarao K. Xu C. Wen Y. Tensor canonical correlation analysis for multi-view dimension reduction 2016 IEEE 32nd International Conference on Data Engineering (ICDE) 2016 10.1109/icde.2016.7498374 · DOI
  112. [137]Tenenhaus M. Tenenhaus A. Groenen P.J.F. Regularized generalized canonical correlation analysis: a framework for sequential multiblock component methods Psychometrika 82 2017 737 777 28536930
  113. [138]Meng C. Kuster B. Culhane A.C. Gholami A.M. A multivariate approach to the integration of multi-omics datasets BMC Bioinf 15 2014 162 PMC4053266 24884486
  114. [139]Chalise P. Ni Y. Fridley B.L. Network-based integrative clustering of multiple types of genomic data using non-negative matrix factorization Comput Biol Med 118 2020 103625 PMC7078030 31999549
  115. [140]Fujita N. Mizuarai S. Murakami K. Nakai K. Biomarker discovery by integrated joint non-negative matrix factorization and pathway signature analyses Sci Rep 8 2018 PMC6021419 29950679
  116. [141]Argelaguet R. Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets Mol Syst Biol 14 2018 PMC6010767 29925568
  117. [142]Argelaguet R. MOFA: a statistical framework for comprehensive integration of multi-modal single-cell data Genome Biol 21 2020 PMC7212577 32393329
  118. [143]Clark C. Dayon L. Masoodi M. Bowman G.L. Popp J. An integrative multi-omics approach reveals new central nervous system pathway alterations in Alzheimer’s disease Alzheimers. Res. Ther. 13 2021 71 33794997 PMC8015070
  119. [144]Palou-Márquez G. Subirana I. Nonell L. Fernández-Sanlés A. Elosua R. DNA methylation and gene expression integration in cardiovascular disease Clin. Epigenetics 13 2021 75 33836805 PMC8034168
  120. [145]Shen R. Olshen A.B. Ladanyi M. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis Bioinformatics 25 2009 2906 2912 19759197 PMC2800366
  121. [147]Mo Q. A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data Biostatistics 19 2018 71 86 28541380 PMC6455926
  122. [148]Ma M. Integrative analysis of genomic, epigenomic and transcriptomic data identified molecular subtypes of esophageal carcinoma Aging 13 2021 6999 7019 33638948 PMC7993659
  123. [149]Zhao Y. Gao Y. Xu X. Zhou J. Wang H. Multi-omics analysis of genomics, epigenomics and transcriptomics for molecular subtypes and core genes for lung adenocarcinoma BMC Cancer 21 2021 257 33750346 PMC7942004
  124. [150]Mo Q. Li R. Adeegbe D.O. Peng G. Chan K.S. Integrative multi-omics analysis of muscle-invasive bladder cancer identifies prognostic biomarkers for frontline chemotherapy and immunotherapy Commun Biol 3 2020 784 33335285 PMC7746703
  125. [151]Lock E.F. Hoadley K.A. Marron J.S. Nobel A.B. Joint and individual variation explained (JIVE) for integrated analysis of multiple data types Ann Appl Statistics 7 2013 PMC3671601 23745156
  126. [153]Kaplan A. Lock E.F. Prediction with dimension reduction of multiple molecular data sources for patient survival Cancer Inf 16 2017 PMC5510774 28747816
  127. [154]Ponzi E, Thoresen M, Nøst, TH, Møllersen K. Integrative analyses of multi-omics data improves model predictions: an application to lung cancer. bioRxiv 2020.10.02.299834 (2020) doi: 10.1101/2020.10.02.299834. · DOI
  128. [155]Sun D. Wang M. Li A. A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data IEEE/ACM Trans Comput Biol Bioinf 16 2019 841 850 29994639
  129. [156]Wang T. et al. MORONET: Multi-omics Integration via Graph Convolutional Networks for Biomedical Data Classification. doi: 10.1101/2020.07.02.184705. · DOI
  130. [157]Wang W. iBAG: integrative Bayesian analysis of high-dimensional multiplatform genomics data Bioinformatics 29 2013 149 159 23142963 PMC3546799
  131. [158]Zhu R. Zhao Q. Zhao H. Ma S. Integrating multidimensional omics data for cancer outcome Biostatistics 17 2016 605 618 26980320 PMC5031941
  132. [159]Chai H. Analysis of cancer gene expression data with an assisted robust marker identification approach Genet Epidemiol 41 2017 779 789 28913902 PMC5912176
  133. [160]Wu C. Zhang Q. Jiang Y. Ma S. Robust network-based analysis of the associations between (epi)genetic measurements J. Multivar. Anal. 168 2018 119 130 30983643 PMC6456078
  134. [161]Kim D-C et al. Integration of DNA Methylation, Copy Number Variation, and Gene Expression for Gene Regulatory Network Inference and Application to Psychiatric Disorders. in 2014 IEEE International Conference on Bioinformatics and Bioengineering 238–242 (2014).
  135. [162]Cai X. Bazerque J.A. Giannakis G.B. Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations PLoS Comput Biol 9 2013 e1003068 PMC3662697 23717196
  136. [163]Fortelny N, Bock C. Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data. doi:10.1101/794503. PMC7397672 32746932 · DOI
  137. [164]Balluff B. Integrative clustering in mass spectrometry imaging for enhanced patient stratification Proteomics Clin Appl 13 2019 e1800137 PMC6590511 30580496
  138. [165]Ma S. Ren J. Fenyö D. Breast cancer prognostics using multi-omics data AMIA Jt Summits Transl Sci Proc 2016 2016 52 59 27570650 PMC5001766
  139. [166]McDonald M.E. Molecular characterization of non-responders to chemotherapy in serous ovarian cancer Int J Mol Sci 20 2019 PMC6429334 30866519
  140. [167]Zhu Y. Ensemble transfer learning for the prediction of anti-cancer drug response Sci Rep 10 2020 18040 33093487 PMC7581765
  141. [168]Herrmann M. Probst P. Hornung R. Jurinovic V. Boulesteix A.-L. Large-scale benchmark study of survival prediction methods using multi-omics data Brief. Bioinform. 2020 10.1093/bib/bbaa167 PMC8138887 32823283 · DOI
  142. [169]Pierre-Jean M. Deleuze J.-F. Le Floch E. Mauger F. Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration Brief. Bioinform. 21 2020 2011 2030 31792509
  143. [170]Wei Z. Zhang Y. Weng W. Chen J. Cai H. Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets Briefings Bioinf 2020 10.1093/bib/bbaa102 32533167 · DOI
  144. [171]McCabe S.D. Lin D.-Y. Love M.I. Consistency and overfitting of multi-omics methods on experimental data Brief. Bioinform. 21 2020 1277 1284 31281919 PMC7373174
  145. [172]Zhu W. Xie L. Han J. Guo X. The application of deep learning in cancer prognosis prediction Cancers 12 2020 PMC7139576 32150991

Cited by

Sorted by citations

Authors
Picard, M., Scott-Boyer, M.-P., Bodein, A., Périn, O., & Droit, A.
Affiliations
  • Université Laval (CA)
  • Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
  • L'Oréal (France) (FR)
Corresponding author
Droit, Arnaud
ORCID
Venue
Computational and Structural Biotechnology Journal
Publisher
American Association for the Advancement of Science (AAAS)
Published
2021-01
Volume
19
Pages
3735-3746
ISSN
2001-0370 (ISSN-L)
DOI
10.1016/j.csbj.2021.06.030
PMC
PMC8258788
OpenAlex
W3173875945
Cited by
696
References
13
Citations by year
2026: 179 · 2025: 186 · 2024: 153 · 2023: 104 · 2022: 65 · 2021: 8 · 2018: 1
FWCI
8.81
Type
journal article
Language
en
Open access
Licence
Locations
  • Computational and Structural Biotechnology Journal (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
UK Research and Innovation
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
multi-omics integration, machine learning, biomarker discovery, early integration, hierarchical integration, genomics, epigenomics, transcriptomics, proteomics, metabolomics

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