The following publications are possibly variants of this publication:
- GCNMFCDA: A Method Based on Graph Convolutional Network and Matrix Factorization for Predicting circRNA-Disease AssociationsDian-Xiao Wang, Cunmei Ji, Yu-Tian Wang, Lei Li, Jian-Cheng Ni, Bin Li. icic 2022: 166-180 [doi]
- Generalized predictive control of DEAP actuator based on RBFNeural Network, Zhaoguo Jiang, Qinglin Wang, Yuan Li. ascc 2017: 1632-1637 [doi]
- Combining K Nearest Neighbor With Nonnegative Matrix Factorization for Predicting Circrna-Disease AssociationsMei-Neng Wang, Xue-Jun Xie, Zhu-Hong You, Leon Wong, Li-ping Li, Zhan-Heng Chen. tcbb, 20(5):2610-2618, September - October 2023. [doi]
- Weighted Nonnegative Matrix Factorization Based on Multi-source Fusion Information for Predicting CircRNA-Disease AssociationsMei-Neng Wang, Xue-Jun Xie, Zhuhong You, Leon Wong, LiPing Li, Zhan-Heng Chen. icic 2021: 467-477 [doi]
- RNMFLP: Predicting circRNA-disease associations based on robust nonnegative matrix factorization and label propagationLi Peng, Cheng Yang, Li Huang, Xiang Chen, Xiangzheng Fu, Wei Liu. bib, 23(5), 2022. [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]
- LMGATCDA: Graph Neural Network With Labeling Trick for Predicting circRNA-Disease AssociationsWenjing Wang, Pengyong Han, ZhengWei Li, Ru Nie, Kangwei Wang, Lei Wang 0121, Hongmei Liao. tcbb, 21(2):289-300, March - April 2024. [doi]