Prediction of Potential MicroRNA–Disease Association Using Kernelized Bayesian Matrix Factorization
Author:
Publisher
Springer Science and Business Media LLC
Subject
Health Informatics,Computer Science Applications,General Biochemistry, Genetics and Molecular Biology
Link
https://link.springer.com/content/pdf/10.1007/s12539-021-00469-w.pdf
Reference46 articles.
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2. Chen X, Zhou Z, Zhao Y (2018) ELLPMDA: ensemble learning and link prediction for miRNA-disease association prediction. RNA Biol 15(6): 807–818. Doi: https://doi.org/10.1080/15476286.2018.1460016. https://pubmed.ncbi.nlm.nih.gov/29619882/
3. Tang C et al (2019) Dual Laplacian regularized matrix completion for MicroRNA–Disease associations prediction. RNA Biol 16(5): 601–611. Doi: https://doi.org/10.1080/15476286.2019.1570811. https://www.ncbi.nlm.nih.gov/pubmed/30676207
4. Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136(2): 215–233. Doi: https://doi.org/10.1016/j.cell.2009.01.002. https://www.sciencedirect.com/science/article/pii/S0092867409000087
5. Lan W et al (2018) Predicting MicroRNA–Disease associations based on improved microRNA and disease similarities. IEEE/ACM Trans Comp Biol Bioinform 15(6): 1774–1782. Doi: https://doi.org/10.1109/TCBB.2016.2586190. https://www.ncbi.nlm.nih.gov/pubmed/27392365
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