LRSSL: predict and interpret drug–disease associations based on data integration using sparse subspace learning | Zendy

Xujun Liang | Zendy; Pengfei Zhang | Zendy; Yan Lü | Zendy; Ying Fu | Zendy; Peng Fang | Zendy; Lingzhi Qu | Zendy; Meiying Shao | Zendy; Yongheng Chen | Zendy; Zhuchu Chen | Zendy

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LRSSL: predict and interpret drug–disease associations based on data integration using sparse subspace learning

Author(s) -

Xujun Liang,

Pengfei Zhang,

Yan Lü,

Ying Fu,

Peng Fang,

Lingzhi Qu,

Meiying Shao,

Yongheng Chen,

Zhuchu Chen

Publication year - 2016

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/btw770

Subject(s) - subspace topology , computer science , drug , machine learning , artificial intelligence , medicine , pharmacology

: Exploring the potential curative effects of drugs is crucial for effective drug development. Previous studies have indicated that integration of multiple types of information could be conducive to discovering novel indications of drugs. However, how to efficiently identify the mechanism behind drug-disease associations while integrating data from different sources remains a challenging problem.

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