title: Machine learning and systems genomics approaches for multi-omics data.
abstract: In light of recent advances in biomedical computing, big data science, and precision medicine, there is a mammoth demand for establishing algorithms in machine learning and systems genomics (MLSG), together with multi-omics data, to weigh probable phenotype-genotype relationships. Software frameworks in MLSG are extensively employed to analyze hundreds of thousands of multi-omics data by high-throughput technologies. In this study, we reviewed the MLSG software frameworks and future directions with respect to multi-omics data analysis and integration. Our review was targeted at researching recent approaches and technical solutions for the MLSG software frameworks using multi-omics platforms.
authors.affiliation: Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan.|Vita Genomics, Inc, Taipei, Taiwan.|TickleFish Systems Corporation, Seattle, WA USA.
authors.forename: Eugene
authors.initials: E
authors.lastname: Lin
authors.affiliation: Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan.|Department of Psychiatry, China Medical University Hospital, Taichung, Taiwan.
authors.forename: Hsien-Yuan
authors.initials: HY
authors.lastname: Lane
keywords: Genomics; Machine learning; Multi-omics; Pharmacogenomics; Single nucleotide polymorphisms; Systems genomics