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Integration strategies of multi-omics data for machine learning analysis
2021
Computational and Structural Biotechnology Journal
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
doi:10.1016/j.csbj.2021.06.030
pmid:34285775
pmcid:PMC8258788
fatcat:n45fkusgnfen3noyrwgklykboq