Prediction of lncRNA–disease associations based on inductive matrix completion | Zendy

Chengqian Lu | Zendy; Mengyun Yang | Zendy; Feng Luo | Zendy; FangXiang Wu | Zendy; Min Li | Zendy; Yi Pan | Zendy; Yaohang Li | Zendy; Jianxin Wang | Zendy

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Prediction of lncRNA–disease associations based on inductive matrix completion

Author(s) -

Chengqian Lu,

Mengyun Yang,

Feng Luo,

FangXiang Wu,

Min Li,

Yi Pan,

Yaohang Li,

Jianxin Wang

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/bty327

Subject(s) - disease , computer science , feature (linguistics) , computational biology , kernel (algebra) , similarity (geometry) , data mining , machine learning , artificial intelligence , biology , medicine , mathematics , linguistics , pathology , combinatorics , image (mathematics) , philosophy

Accumulating evidences indicate that long non-coding RNAs (lncRNAs) play pivotal roles in various biological processes. Mutations and dysregulations of lncRNAs are implicated in miscellaneous human diseases. Predicting lncRNA-disease associations is beneficial to disease diagnosis as well as treatment. Although many computational methods have been developed, precisely identifying lncRNA-disease associations, especially for novel lncRNAs, remains challenging.

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