Machine learning and systems genomics approaches for multi-omics data

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• 作者：Eugene Lin ; Hsien-Yuan Lane
• 关键词：Genomics ; Pharmacogenomics ; Single nucleotide polymorphisms ; Machine learning ; Multi ; omics ; Systems genomics
• 刊名：Biomarker Research
• 出版年：2017
• 出版时间：December 2017
• 年：2017
• 卷：5
• 期：1
• 全文大小：589KB
• 刊物主题：Biomedicine, general; Cancer Research; Neurosciences;
• 出版者：BioMed Central
• ISSN：2050-7771
• 卷排序：5

文摘

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.