The following publications are possibly variants of this publication:
- LMTRDA: Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similaritiesLei Wang, Zhu-Hong You, Xing Chen, Yang-Ming Li, Ya-Nan Dong, Li-ping Li, Kai Zheng 0020. ploscb, 15(3), 2019. [doi]
- Predicting miRNA-disease associations based on graph attention network with multi-source informationGuanghui Li 0003, Tao Fang, Yuejin Zhang, Cheng Liang, Qiu Xiao, Jiawei Luo 0001. bmcbi, 23(1):244, 2022. [doi]
- Predicting miRNA-disease associations via learning multimodal networks and fusing mixed neighborhood informationZhengzheng Lou, Zhaoxu Cheng, Hui Li, Zhixia Teng, Yang Liu, Zhen Tian. bib, 23(5), 2022. [doi]
- A Method Based On Dual-Network Information Fusion to Predict MiRNA-Disease AssociationsFeng Zhou, Meng-Meng Yin, Jing-Xiu Zhao, Junliang Shang, Jin-Xing Liu. tcbb, 20(1):52-60, January - February 2023. [doi]
- HOPEXGB: A Consensual Model for Predicting miRNA/lncRNA-Disease Associations Using a Heterogeneous Disease-miRNA-lncRNA Information NetworkJian He, Menglong Li, Jiangguo Qiu, Xuemei Pu, Yanzhi Guo. jcisd, 64(7):2863-2877, 2024. [doi]