Graph regularized L 2,1 -nonnegative matrix factorization for miRNA-disease association prediction.
Autor: | Gao Z; School of Software, Qufu Normal University, Qufu, 273165, China., Wang YT; School of Software, Qufu Normal University, Qufu, 273165, China., Wu QW; School of Software, Qufu Normal University, Qufu, 273165, China., Ni JC; School of Software, Qufu Normal University, Qufu, 273165, China. nijch@163.com., Zheng CH; School of Software, Qufu Normal University, Qufu, 273165, China. zhengch99@126.com. |
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Jazyk: | angličtina |
Zdroj: | BMC bioinformatics [BMC Bioinformatics] 2020 Feb 18; Vol. 21 (1), pp. 61. Date of Electronic Publication: 2020 Feb 18. |
DOI: | 10.1186/s12859-020-3409-x |
Abstrakt: | Background: The aberrant expression of microRNAs is closely connected to the occurrence and development of a great deal of human diseases. To study human diseases, numerous effective computational models that are valuable and meaningful have been presented by researchers. Results: Here, we present a computational framework based on graph Laplacian regularized L Conclusions: The new method (GRL |
Databáze: | MEDLINE |
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