Machine learning and systems genomics approaches for multi-omics data

被引：118
作者：
Lin, Eugene [1 ,2 ,3 ]
Lane, Hsien-Yuan [1 ,4 ]
机构：
[1] China Med Univ, Grad Inst Biomed Sci, Taichung, Taiwan
[2] Vita Genom Inc, Taipei, Taiwan
[3] TickleFish Syst Corp, Seattle, WA USA
[4] China Med Univ Hosp, Dept Psychiat, Taichung, Taiwan
来源：
BIOMARKER RESEARCH | 2017年 / 5卷
关键词：
Genomics; Pharmacogenomics; Single nucleotide polymorphisms; Machine learning; Multi-omics; Systems genomics; GENE-GENE INTERACTIONS; TAIWANESE POPULATION; HAPLOTYPE ANALYSIS; FEATURE-SELECTION; PUBLIC-HEALTH; DRUG EFFICACY; MODEL; PHARMACOGENOMICS; CLASSIFICATION; REGULARIZATION;
D O I：
10.1186/s40364-017-0082-y
中图分类号：
R73 [肿瘤学];
学科分类号：
100214 ;
摘要：
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.
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页数：6
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