iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach | Zendy

Bin Liu | Zendy; Kai Li | Zendy; De-Shuang Huang | Zendy; KuoChen Chou | Zendy

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iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach

Author(s) -

Bin Liu,

Kai Li,

De-Shuang Huang,

KuoChen Chou

Publication year - 2018

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/bty458

Subject(s) - computer science , support vector machine , enhancer , key (lock) , subsequence , data mining , artificial intelligence , machine learning , pattern recognition (psychology) , gene , biology , mathematics , gene expression , genetics , mathematical analysis , computer security , bounded function

Identification of enhancers and their strength is important because they play a critical role in controlling gene expression. Although some bioinformatics tools were developed, they are limited in discriminating enhancers from non-enhancers only. Recently, a two-layer predictor called 'iEnhancer-2L' was developed that can be used to predict the enhancer's strength as well. However, its prediction quality needs further improvement to enhance the practical application value.

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