Data integration and predictive modeling methods for multi-omics datasets. Issue 1 (20th December 2017)

• Record Type: Journal Article
• Authors: Kim, Minseung; Tagkopoulos, Ilias
• Is Part Of: Molecular omics. Volume 14:Issue 1(2018)
• Page Start: 8
• Page End: 25
• Publication Date: 2017-12-20
• Subjects: Molecular biology -- Periodicals; Biochemistry -- Periodicals; Biological systems -- Periodicals; Molecular Biology; Computational Biology; Biochemistry; Biological systems; Molecular biology; Periodicals; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals
• DOI: 10.1039/c7mo00051k
• Languages: English
• ISSNs: 2515-4184

Abstract

Abstract : We provide an overview of opportunities and challenges in multi-omics predictive analytics with particular emphasis on data integration and machine learning methods. Abstract : Translating data to knowledge and actionable insights is the Holy Grail for many scientific fields, including biology. The unprecedented massive and heterogeneous data have created as many challenges to store, process and analyze as the opportunities and promises they hold. Here, we provide an overview of these opportunities and challenges in multi-omics predictive analytics.