iEnhancer-XG: interpretable sequence-based enhancers and their strength predictor | Zendy

Lijun Cai | Zendy; Xuanbai Ren | Zendy; Xiangzheng Fu | Zendy; Peng Li | Zendy; Mingyu Gao | Zendy; Xiangxiang Zeng | Zendy

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iEnhancer-XG: interpretable sequence-based enhancers and their strength predictor

Author(s) -

Lijun Cai,

Xuanbai Ren,

Xiangzheng Fu,

Peng Li,

Mingyu Gao,

Xiangxiang Zeng

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa914

Subject(s) - computer science , interpretability , source code , classifier (uml) , support vector machine , ensemble learning , artificial intelligence , machine learning , data mining , pattern recognition (psychology) , operating system

Enhancers are non-coding DNA fragments with high position variability and free scattering. They play an important role in controlling gene expression. As machine learning has become more widely used in identifying enhancers, a number of bioinformatic tools have been developed. Although several models for identifying enhancers and their strengths have been proposed, their accuracy and efficiency have yet to be improved.

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