Revealing new therapeutic opportunities through drug target prediction: a class imbalance-tolerant machine learning approach | Zendy

Siqi Liang | Zendy; Haiyuan Yu | Zendy

Open Access

Revealing new therapeutic opportunities through drug target prediction: a class imbalance-tolerant machine learning approach

Author(s) -

Siqi Liang,

Haiyuan Yu

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/btaa495

Subject(s) - drug repositioning , druggability , computer science , drugbank , repurposing , machine learning , drug , classifier (uml) , in silico , computational biology , artificial intelligence , drug discovery , cheminformatics , drug target , gene , bioinformatics , biology , genetics , pharmacology , ecology

In silico drug target prediction provides valuable information for drug repurposing, understanding of side effects as well as expansion of the druggable genome. In particular, discovery of actionable drug targets is critical to developing targeted therapies for diseases.

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