Affiliation:
1. Computational Biology and Bioinformatics Laboratory Department of Integrative Biotechnology College of Biotechnology and Bioengineering Sungkyunkwan University Suwon Gyeonggi‐do South Korea
2. Beidahuang Industry Group General Hospital Harbin China
Abstract
AbstractEnhancers are non‐coding DNA elements that play a crucial role in enhancing the transcription rate of a specific gene in the genome. Experiments for identifying enhancers can be restricted by their conditions and involve complicated, time‐consuming, laborious, and costly steps. To overcome these challenges, computational platforms have been developed to complement experimental methods that enable high‐throughput identification of enhancers. Over the last few years, the development of various enhancer computational tools has resulted in significant progress in predicting putative enhancers. Thus, researchers are now able to use a variety of strategies to enhance and advance enhancer study. In this review, an overview of machine learning (ML)‐based prediction methods for enhancer identification and related databases has been provided. The existing enhancer‐prediction methods have also been reviewed regarding their algorithms, feature selection processes, validation techniques, and software utility. In addition, the advantages and drawbacks of these ML approaches and guidelines for developing bioinformatic tools have been highlighted for a more efficient enhancer prediction. This review will serve as a useful resource for experimentalists in selecting the appropriate ML tool for their study, and for bioinformaticians in developing more accurate and advanced ML‐based predictors.
Funder
National Research Foundation of Korea
Subject
Molecular Biology,Biochemistry
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献