Predicting multiple types of miRNA-disease associations using adaptive weighted nonnegative tensor factorization with self-paced learning and hypergraph regularization.
- DOI
- 10.1093/bib/bbac390
- Published
- 2022 Nov 19
- Container
- Briefings in bioinformatics
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1093/bib/bbac390,
title = {Predicting multiple types of miRNA-disease associations using adaptive weighted nonnegative tensor factorization with self-paced learning and hypergraph regularization.},
author = {Ouyang D and Liang Y and Wang J and Liu X and Xie S and Miao R and Ai N and Li L and Dang Q},
year = {2022},
journal = {Briefings in bioinformatics},
doi = {10.1093/bib/bbac390},
url = {https://doi.org/10.1093/bib/bbac390}
}RIS
TY - JOUR TI - Predicting multiple types of miRNA-disease associations using adaptive weighted nonnegative tensor factorization with self-paced learning and hypergraph regularization. AU - Ouyang D AU - Liang Y AU - Wang J AU - Liu X AU - Xie S AU - Miao R AU - Ai N AU - Li L AU - Dang Q PY - 2022 JO - Briefings in bioinformatics DO - 10.1093/bib/bbac390 UR - https://doi.org/10.1093/bib/bbac390 ER -
APA
D, O., Y, L., J, W., X, L., S, X., R, M., N, A., L, L., & Q, D. (2022). Predicting multiple types of miRNA-disease associations using adaptive weighted nonnegative tensor factorization with self-paced learning and hypergraph regularization.. Briefings in bioinformatics. https://doi.org/10.1093/bib/bbac390
Source records
- pubmed · retrieved 2026-09-25T13:01:22.814Z