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Brief Bioinform. 2018 Nov 16. doi: 10.1093/bib/bby110. [Epub ahead of print]

A comprehensive review of computational prediction of genome-wide features.

Author information

1
Department of Mathematics and Computer Science, Emory University, Atlanta, GA, USA.
2
Department of Mathematics, Shanghai Normal University, Shanghai, China.
3
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
4
Department of Human Genetics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.

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.

PMID:
30462144
DOI:
10.1093/bib/bby110

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