Matrix factorization-based data fusion for the prediction of lncRNA–disease associations | Zendy

Guangyuan Fu | Zendy; Jun Wang | Zendy; Carlotta Domeniconi | Zendy; Guoxian Yu | Zendy

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Matrix factorization-based data fusion for the prediction of lncRNA–disease associations

Author(s) -

Guangyuan Fu,

Jun Wang,

Carlotta Domeniconi,

Guoxian Yu

Publication year - 2017

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/btx794

Subject(s) - computer science , matrix decomposition , fusion , sensor fusion , matrix (chemical analysis) , artificial intelligence , data mining , chemistry , physics , eigenvalues and eigenvectors , quantum mechanics , philosophy , linguistics , chromatography

Long non-coding RNAs (lncRNAs) play crucial roles in complex disease diagnosis, prognosis, prevention and treatment, but only a small portion of lncRNA-disease associations have been experimentally verified. Various computational models have been proposed to identify lncRNA-disease associations by integrating heterogeneous data sources. However, existing models generally ignore the intrinsic structure of data sources or treat them as equally relevant, while they may not be.

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