Machine learning for profile prediction in genomics. (December 2021)

• Record Type: Journal Article
• Authors: Schreiber, Jacob; Singh, Ritambhara
• Is Part Of: Current opinion in chemical biology. Volume 65(2021)
• Page Start: 35
• Page End: 41
• Publication Date: 2021-12
• Subjects: Regulatory mechanisms -- Profile prediction -- Prediction tasks -- Motif detection -- Neural networks -- Interpretation -- Imputation; Bioorganic chemistry -- Periodicals; Biology -- Periodicals; Biochemistry -- Periodicals; Clinical biochemistry -- Periodicals; Biochemistry -- Periodicals; Chimie bio-organique -- Périodiques; Biologie -- Périodiques; 572.05
• DOI: 10.1016/j.cbpa.2021.04.008
• Languages: English
• ISSNs: 1367-5931

Abstract

Abstract: A recent deluge of publicly available multi-omics data has fueled the development of machine learning methods aimed at investigating important questions in genomics. Although the motivations for these methods vary, a task that is commonly adopted is that of profile prediction, where predictions are made for one or more forms of biochemical activity along the genome, for example, histone modification, chromatin accessibility, or protein binding. In this review, we give an overview of the research works performing profile prediction, define two broad categories of profile prediction tasks, and discuss the types of scientific questions that can be answered in each.