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[11] Zhong, Tangbo,李政伟,You, Zhu-Hong,聂茹,Zhao, Huan.Predicting miRNA-disease associations based on graph random propagation network and attention network,2022,卷: 23期: 2
[12] Predicting miRNA-disease associations based on graph random propagation network and attention network.Briefings in Bioinformatics,2022
[13] Xu, Caixia,Yang, Xiaokun,Wang, Zhipeng,Han, Pengyong,Li, Xiaoguang,李政伟.Research on the Potential Mechanism of Rhizoma Drynariae in the Treatment of Periodontitis Based on Network Pharmacology,2022,v 13394 LNCS,p207-219
[14] Zhang, Zhengtao,Han, Pengyong,李政伟,聂茹,Wang, Qiankun.Prediction of MiRNA-Disease Association Based on Higher-Order Graph Convolutional Networks,2022,v 13394 LNCS,p181-195
[15] Tian, Yun,Xu, Caixia,Li, Lin,Han, Pengyong,李政伟.The CNV Predict Model in Esophagus Cancer,2022,v 13394 LNCS,p405-414
[16] Liu, Hailei,Chen, Zhenqiong,Gopalakrishnan, Chandrasekhar,Ramalingam, Rajasekaran,Han, Pengyong,李政伟.The Prognosis Model of Clear Cell Renal Cell Carcinoma Based on Allograft Rejection Markers,2022,v 13394 LNCS,p383-393
[17] Gopalakrishnan, Chandrasekhar,Xu, Caixia,Li, Yanran,Anandhan, Vinutha,Gangadharan, Sanjay,Paul, Meshach,Ponnusamy, Chandra Sekar,Ramalingam, Rajasekaran,Han, Pengyong,李政伟.Elucidating Quantum Semi-empirical Based QSAR, for Predicting Tannins’ Anti-oxidant Activity with the Help of Artificial Neural Network,2022,v 13394 LNCS,p289-301
[18] 李政伟,李佳树,聂茹,尤著宏,鲍文正.A graph auto-encoder model for miRNA-disease associations prediction,2021,卷: 22期: 4
[19] Ji, Bo-Ya,You, Zhu-Hong,Wang, Yi,李政伟,Wong, Leon.DANE-MDA: Predicting microRNA-disease associations via deep attributed network embedding,2021,卷: 24期: 6
[20] Li, Hao-Yuan,You, Zhu-Hong,Wang, Lei,Yan, Xin,李政伟.DF-MDA: An effective diffusion-based computational model for predicting miRNA-disease association,2021,卷: 29期: 4页: 1501-1511

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