The following publications are possibly variants of this publication:
- RCMF: a robust collaborative matrix factorization method to predict miRNA-disease associationsZhen Cui, Jin-Xing Liu, Ying-Lian Gao, Chun-Hou Zheng, Juan Wang 0003. bmcbi, 20-S(25):686, 2019. [doi]
- NPCMF: Nearest Profile-based Collaborative Matrix Factorization method for predicting miRNA-disease associationsYing-Lian Gao, Zhen Cui, Jin-Xing Liu, Juan Wang, Chun-Hou Zheng. bmcbi, 20(1), 2019. [doi]
- Predicting miRNA-disease association based on inductive matrix completionXing Chen, Lei Wang, Jia Qu, Na-Na Guan, Jian-qiang Li. bioinformatics, 34(24):4256-4265, 2018. [doi]
- A New Method Based on Matrix Completion and Non-Negative Matrix Factorization for Predicting Disease-Associated miRNAsZhen Gao, Yu-Tian Wang, Qing-Wen Wu, Lei Li, Jian-Cheng Ni, Chun-Hou Zheng 0001. tcbb, 19(2):763-772, 2022. [doi]
- Predicting CircRNA-Disease Associations Through Linear Neighborhood Label Propagation MethodWen Zhang, Chenglin Yu, Xiaochan Wang, Feng Liu. access, 7:83474-83483, 2019. [doi]
- A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer networkYingli Zhong, Ping Xuan, Xiao Wang, Tiangang Zhang, Jianzhong Li, Yong Liu, Weixiong Zhang. bioinformatics, 34(2):267-277, 2018. [doi]