Machine learning and systems genomics approaches for multi-omics data

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 framework...

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Bibliographic Details
Published in:Biomarker research 2017-01, Vol.5 (1), p.2-2, Article 2
Main Authors: Lin, Eugene, Lane, Hsien-Yuan
Format: Article
Language:English
Subjects:
Analysis
Health aspects
Machine learning
Pharmacogenomics
Precision medicine
Review
Single nucleotide polymorphisms
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Summary: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.
ISSN:2050-7771
2050-7771
DOI:10.1186/s40364-017-0082-y