期刊
BRIEFINGS IN BIOINFORMATICS
卷 21, 期 1, 页码 120-134出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bby110
关键词
machine learning; genomic features; prediction model
资金
- National Institutes of Health [R01GM122083, U54NS091859, P01NS097206]
- National Natural Science Foundation of China [61572327]
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.
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