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


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.

SUBMITTER: Xu T 

PROVIDER: S-EPMC10233247 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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

Xu Tianlei T   Zheng Xiaoqi X   Li Ben B   Jin Peng P   Qin Zhaohui Z   Wu Hao H  

Briefings in bioinformatics 20200101 1


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, categorize  ...[more]

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