Integration strategies of multi-omics data for machine learning analysis

被引：0
作者：
Picard M. [1 ]
Scott-Boyer M.-P. [1 ]
Bodein A. [1 ]
Périn O. [2 ]
Droit A. [1 ]
机构：
[1] Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC
[2] Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois
来源：
Computational and Structural Biotechnology Journal | 2021年 / 19卷
基金：
英国科研创新办公室;
关键词：
Deep learning; Integration strategy; Machine learning; Multi-omics; Multi-view; Network;
D O I：
10.1016/j.csbj.2021.06.030
中图分类号：
学科分类号：
摘要：
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. © 2021 The Author(s)
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页码：3735 / 3746
页数：11
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