LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction

Predicting novel microRNA (miRNA)-disease associations is clinically significant due to miRNAs' potential roles of diagnostic biomarkers and therapeutic targets for various human diseases. Previous studies have demonstrated the viability of utilizing different types of biological data to comput...

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Veröffentlicht in:	PLoS computational biology 2017-12, Vol.13 (12), p.e1005912-e1005912
Hauptverfasser:	Chen, Xing, Huang, Li
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Apoptosis Authorship Biological computing Biology and life sciences Biomarkers Breast cancer Case studies Cell growth Colon Colorectal cancer Computation Computer applications Datasets Diagnostic systems Disease Esophageal cancer Esophagus Gene expression Genetic susceptibility Health aspects Kidney cancer Kidneys Kinases Lymphoma Mathematical models Medical research Medicine and Health Sciences MicroRNA MicroRNAs miRNA Model accuracy Neoplasms Physical Sciences Predictions Regularization Research and Analysis Methods Research methodology Ribonucleic acid RNA Signal transduction Social Sciences Software Subspaces Tumors Viability
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