A comprehensive review of computational prediction of genome-wide features. (16th November 2018)

• Record Type: Journal Article
• Authors: Xu, Tianlei; Zheng, Xiaoqi; Li, Ben; Jin, Peng; Qin, Zhaohui; Wu, Hao
• Is Part Of: Briefings in bioinformatics. Volume 21:Number 1(2020)
• Page Start: 120
• Page End: 134
• Publication Date: 2018-11-16
• Subjects: machine learning -- genomic features -- prediction model; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bby110
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: There are significant correlations among different types of genetic, genomic and epigenomic features within the genome. These correlations make the in silico feature prediction possible through statistical or machine learning models. With the accumulation of a vast amount of high-throughput data, feature prediction has gained significant interest lately, and a plethora of papers have been published in the past few years. Here we provide a comprehensive review on these published works, categorized by the prediction targets, including protein binding site, enhancer, DNA methylation, chromatin structure and gene expression. We also provide discussions on some important points and possible future directions.