Publications

2024

  • Zhenxiang Gao#, Pingjian Ding#, and Rong Xu*. “IUPHAR review-Data-driven Computational Drug Repurposing Approaches for Opioid Use Disorder.” Pharmacological Research 199 (2024): 106960. [Link]

  • Hanyu Luo, Li Tang, Min Zeng, Rui Yin, Pingjian Ding, Lingyun Luo, and Min Li. “BertSNR: an interpretable deep learning framework for single nucleotide resolution identification of transcription factor binding sites based on DNA language model.” Bioinformatics (2024). [Link]

  • Anlin Hou, Hanyu Luo, Huan Liu, Lingyun Luo, and Pingjian Ding. “Multi-scale DNA language model improves 6 mA binding sites prediction.” Computational Biology and Chemistry 112(17):108129. (2024). [Link]

  • Zhenyu Jiang, Pingjian Ding, Cong Shen, and Xiaopeng Dai. “Geometric molecular graph representation learning model for drug-drug interactions prediction.” (2024). IEEE Journal of Biomedical and Health Informatics. [Link]

  • Yuxun Luo, Wenyu Shan, Li Peng, Lingyun Luo, Pingjian Ding, and Wei Liang. “A computational framework for predicting novel drug indications using graph convolutional network with contrastive learning.” IEEE Journal of Biomedical and Health Informatics (2024). [Link]

  • Ziyu Wu, Shasha Li, Lingyun Luo, and Pingjian Ding*. “HKFGCN: A novel multiple kernel fusion framework on graph convolutional network to predict microbe-drug associations.” Computational Biology and Chemistry (2024): 108041. [Link]

  • Yuxun Luo, Shasha Li, Li Peng, Pingjian Ding, and Wei Liang. “Predicting associations between drugs and G Protein-Coupled Receptors using a multi-graph convolutional network.” Computational Biology and Chemistry (2024): 108060. [Link]

  • Cong Shen, Pingjian Ding, Junjie Wee, Jialin Bi, Jiawei Luo, and Kelin Xiaa. “Curvature-enhanced graph convolutional network for biomolecular interaction prediction.” Computational and Structural Biotechnology Journal (2024). [Link]

  • Min Li, Baoying Zhao, Yiming Li, Pingjian Ding, Rui Yin, Shichao Kan, Yi Pan, and Min Zeng. “SGCL-LncLoc: an interpretable deep learning model for improving lncRNA subcellular localization prediction with supervised graph contrastive learning.” Big Data Mining and Analytics (2024). [Link]

  • Jiang, Zhenyu, Zhi Gong, Xiaopeng Dai, Hongyan Zhang, Pingjian Ding, and Cong Shen. “Deep graph contrastive learning model for drug-drug interaction prediction.” PloS one 19, no. 6 (2024): e0304798. [Link]

2023

  • Pan Zheng*, Xiangxiang Zeng, Xun Wang, Pingjian Ding. “Editorial: Artificial intelligence and machine learning for drug discovery, design and repurposing: methods and applications.” Frontiers in Pharmacology (2023). [Link]

  • Pingjian Ding, and Rong Xu*. “Causal association of COVID-19 with brain structure changes: Findings from a non-overlapping 2-sample Mendelian randomization study.” Journal of the Neurological Sciences (2023): 2023-07. [Link]

  • Pingjian Ding, Mark Gurney, George Perry, Rong Xu*. “Association of COVID-19 with risk and progression of Alzheimer’s disease: non-overlapping two-sample Mendelian randomization analysis of 2.6 million subjects.” Journal of Alzheimer’s Disease (2023). [Link]

  • Wenyu Shan, Cong Shen, Lingyun Luo, and Pingjian Ding*. “Multi-task Learning for Predicting Synergistic Drug Combinations based on Auto-Encoding Multi-Relational Graphs.” iScience (2023). [Link]

  • Yichen Zhong, Xiaoting Xi, Cong Shen, Yuxun Luo, Pingjian Ding, and Lingyun Luo*. “Multitask joint learning with graph autoencoders for predicting potential MiRNA-drug associations.” Artificial Intelligence in Medicine (2023): 102665. [Link]

  • Cheng Chen, Lingyun Luo*, Chunlei Zheng, Pingjian Ding*, Huan Liu, and Hanyu Luo*. “Self-prediction of relations in GO facilitates its quality auditing.” Journal of Biomedical Informatics 144 (2023): 104441. [Link]

  • Hanyu Luo, Ye Li, Huan Liu, Pingjian Ding, Ying Yu, and Lingyun Luo*. “SENet: a deep learning framework for discriminating super-and typical enhancers by sequence information.” Computational Biology and Chemistry (2023): 107905. [Link]

  • Hanyu Luo, Wenyu Shan, Cheng Chen, Pingjian Ding, and Lingyun Luo*. “Improving language model of human genome for DNA–protein binding prediction based on task-specific pre-training.” Interdisciplinary Sciences: Computational Life Sciences 15, no. 1 (2023): 32-43. [Link]

  • Pingjian Ding*, Min Zeng*, and Rui Yin*. “Editorial: Computational methods to analyze RNA data for human diseases.” Frontiers in Genetics 14 (2023). [Link]

  • Pingjian Ding, Maria P. Gorenflo, Xiaofeng Zhu, and Rong Xu*. “Aspirin Use and Risk of Alzheimer’s Disease: A 2-Sample Mendelian Randomization Study.” Journal of Alzheimer’s Disease (2023): 1-12. [Link]

2022

  • Pingjian Ding#, Yiheng Pan#, Quanqiu Wang, and Rong Xu*. “Prediction and evaluation of combination pharmacotherapy using natural language processing, machine learning and patient electronic health records.” Journal of Biomedical Informatics 133 (2022): 104164. [Link]

  • Hanyu Luo, Cheng Chen, Wenyu Shan, Pingjian Ding, and Lingyun Luo*. “iEnhancer-BERT: A novel transfer learning architecture based on DNA-Language model for identifying enhancers and their strength.” In International Conference on Intelligent Computing, pp. 153-165. Cham: Springer International Publishing, 2022. [Link]

  • Zhenxiang Gao, Pingjian Ding, and Rong Xu*. “KG-Predict: A knowledge graph computational framework for drug repurposing.” Journal of biomedical informatics 132 (2022): 104133. [Link]

  • Zhenxiang Gao, Yiheng Pan, Pingjian Ding, and Rong Xu*. “A knowledge graph-based disease-gene prediction system using multi-relational graph convolution networks.” In AMIA Annual Symposium Proceedings, vol. 2022, p. 468. American Medical Informatics Association, 2022. [Link]

2021

  • Xin Chen, Lingyun Luo, Cong Shen, Pingjian Ding*, and Jiawei Luo. “An in silico method for predicting drug synergy based on multitask learning.” Interdisciplinary Sciences: Computational Life Sciences 13 (2021): 299-311. [Link]

  • Pingjian Ding, Cheng Liang, Wenjue Ouyang, Guanghui Li, Qiu Xiao, and Jiawei Luo*. “Inferring synergistic drug combinations based on symmetric meta-path in a novel heterogeneous network.” IEEE/ACM Transactions on Computational Biology and Bioinformatics 18, no. 4 (2019): 1562-1571. [Link]

2020

  • Cong Shen, Jiawei Luo*, Wenjue Ouyang, Pingjian Ding, and Xiangtao Chen. “IDDkin: network-based influence deep diffusion model for enhancing prediction of kinase inhibitors.” Bioinformatics 36, no. 22-23 (2020): 5481-5491. [Link]

  • Li, Guanghui, Jiawei Luo*, Diancheng Wang, Cheng Liang, Qiu Xiao, Pingjian Ding, and Hailin Chen. “Potential circRNA-disease association prediction using DeepWalk and network consistency projection.” Journal of biomedical informatics 112 (2020): 103624. [Link]

  • Cong Shen, Jiawei Luo*, Wenjue Ouyang, Pingjian Ding, and Hao Wu. “Identification of small molecule–miRNA associations with graph regularization techniques in heterogeneous networks.” Journal of Chemical Information and Modeling 60, no. 12 (2020): 6709-6721. [Link]

  • Qiu Xiao, Haiming Yu, Jiancheng Zhong, Cheng Liang, Guanghui Li, Pingjian Ding, and Jiawei Luo*. “An in-silico method with graph-based multi-label learning for large-scale prediction of circRNA-disease associations.” Genomics 112, no. 5 (2020): 3407-3415. [Link]

  • Cong Shen, Jiawei Luo, Zihan Lai, and Pingjian Ding. “Multiview joint learning-based method for identifying small-molecule-associated MiRNAs by integrating pharmacological, genomics, and network knowledge.” Journal of Chemical Information and Modeling 60, no. 8 (2020): 4085-4097. [Link]

  • Pingjian Ding, Wenjue Ouyang, Jiawei Luo*, and Chee-Keong Kwoh. “Heterogeneous information network and its application to human health and disease.” Briefings in bioinformatics 21, no. 4 (2020): 1327-1346. [Link]

  • Jiawei Luo*, Cong Shen, Zihan Lai, Jie Cai, and Pingjian Ding. “Incorporating clinical, chemical and biological information for predicting small molecule-microRNA associations based on non-negative matrix factorization.” IEEE/ACM Transactions on Computational Biology and Bioinformatics 18, no. 6 (2020): 2535-2545. [Link]

  • Haojiang Tan, Quanmeng Sun, Guanghui Li*, Qiu Xiao, Pingjian Ding, Jiawei Luo, and Cheng Liang. “Multiview consensus graph learning for lncRNA–disease association prediction.” Frontiers in genetics 11 (2020): 89. [Link]

  • Pingjian Ding, Cong Shen, Zihan Lai, Cheng Liang, Guanghui Li, and Jiawei Luo*. “Incorporating multisource knowledge to predict drug synergy based on graph co-regularization.” Journal of Chemical Information and Modeling 60, no. 1 (2019): 37-46. [Link]

2019

  • Zhenxia Pan, Huaxiang Zhang, Cheng Liang*, Guanghui Li, Qiu Xiao, Pingjian Ding, and Jiawei Luo. “Self-weighted multi-kernel multi-label learning for potential miRNA-disease association prediction.” Molecular Therapy-Nucleic Acids 17 (2019): 414-423. [Link]

  • Guanghui Li*, Jiawei Luo, Cheng Liang, Qiu Xiao, Pingjian Ding, and Yuejin Zhang. “Prediction of LncRNA-disease associations based on network consistency projection.” Ieee Access 7 (2019): 58849-58856. [Link]

  • Shengpeng Yu, Cheng Liang*, Qiu Xiao, Guanghui Li, Pingjian Ding, and JiaWei Luo. “MCLPMDA: A novel method for mi RNA‐disease association prediction based on matrix completion and label propagation.” Journal of cellular and molecular medicine 23, no. 2 (2019): 1427-1438. [Link]

  • Guanghui Li*, Yingjie Yue, Cheng Liang, Qiu Xiao, Pingjian Ding, and Jiawei Luo. “NCPCDA: network consistency projection for circRNA–disease association prediction.” RSC advances 9, no. 57 (2019): 33222-33228. [Link]

  • Pingjian Ding, Rui Yin, Jiawei Luo*, and Chee-Keong Kwoh. “Ensemble prediction of synergistic drug combinations incorporating biological, chemical, pharmacological, and network knowledge.” IEEE journal of biomedical and health informatics 23, no. 3 (2019): 1336-1345.[Link]

  • Ying Liu, Jiawei Luo*, and Pingjian Ding. “Inferring microRNA targets based on restricted Boltzmann machines.” IEEE journal of biomedical and health informatics 23, no. 1 (2019): 427-436.[Link]

2018

  • Shengpeng Yu, Cheng Liang*, Qiu Xiao, Guanghui Li, Pingjian Ding, and Jiawei Luo. “GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods.” RNA biology 15, no. 9 (2018): 1215-1227. [Link]

  • Xiao, Qiu, Jiawei Luo*, Cheng Liang, Guanghui Li, Jie Cai, Pingjian Ding, and Ying Liu. “Identifying lncRNA and mRNA co-expression modules from matched expression data in ovarian cancer.” IEEE/ACM transactions on computational biology and bioinformatics 17, no. 2 (2018): 623-634. [Link]

  • Yu Qu, Huaxiang Zhang, Cheng Liang*, Pingjian Ding, and Jiawei Luo. “SNMDA: A novel method for predicting micro RNA‐disease associations based on sparse neighbourhood.” Journal of Cellular and Molecular Medicine 22, no. 10 (2018): 5109-5120. [Link]

  • Buwen Cao*, Shuguang Deng, Hua Qin, Pingjian Ding, Shaopeng Chen, and Guanghui Li. “Detection of protein complexes based on penalized matrix decomposition in a sparse protein–protein interaction network.” Molecules 23, no. 6 (2018): 1460. [Link]

  • Jiawei Luo*, Pingjian Ding, Cheng Liang, and Xiangtao Chen. “Semi-supervised prediction of human miRNA-disease association based on graph regularization framework in heterogeneous networks.” Neurocomputing 294 (2018): 29-38. [Link]

  • Guanghui Li*, Jiawei Luo, Qiu Xiao, Cheng Liang, and Pingjian Ding. “Predicting microRNA-disease associations using label propagation based on linear neighborhood similarity.” Journal of biomedical informatics 82 (2018): 169-177. [Link]

  • Pingjian Ding, Jiawei Luo*, Cheng Liang, Qiu Xiao, Buwen Cao, and Guanghui Li. “Discovering synergistic drug combination from a computational perspective.” Current Topics in Medicinal Chemistry 18, no. 12 (2018): 965-974. [Link]

  • Pingjian Ding, Jiawei Luo*, Cheng Liang, Qiu Xiao, and Buwen Cao. “Human disease MiRNA inference by combining target information based on heterogeneous manifolds.” Journal of biomedical informatics 80 (2018): 26-36. [Link]

  • Qiu Xiao, Jiawei Luo*, Cheng Liang, Jie Cai, and Pingjian Ding. “A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations.” Bioinformatics 34, no. 2 (2018): 239-248. [Link]

  • Qiao Zhu, Jiawei Luo*, Pingjian Ding, and Qiu Xiao. “GRTR: Drug-disease association prediction based on graph regularized transductive regression on heterogeneous network.” In Bioinformatics Research and Applications: 14th International Symposium, ISBRA 2018, Beijing, China, June 8-11, 2018, Proceedings 14, pp. 13-25. Springer International Publishing, 2018. [Link]

  • Guanghui Li, Jiawei Luo*, Qiu Xiao, Cheng Liang, and Pingjian Ding. “Prediction of microRNA–disease associations with a Kronecker kernel matrix dimension reduction model.” RSC advances 8, no. 8 (2018): 4377-4385. [Link]

2017

  • Guanghui Li, Jiawei Luo*, Qiu Xiao, Cheng Liang, Pingjian Ding, and Buwen Cao. “Predicting microrna-disease associations using network topological similarity based on deepwalk.” Ieee Access 5 (2017): 24032-24039. [Link]

  • Jiawei Luo*, Qiu Xiao, Cheng Liang, and Pingjian Ding. “Predicting MicroRNA-disease associations using Kronecker regularized least squares based on heterogeneous omics data.” Ieee Access 5 (2017): 2503-2513. [Link]

  • Pingjian Ding, Jiawei Luo*, Cheng Liang, Jie Cai, Ying Liu, and Xiangtao Chen. “A novel group wise-based method for calculating human miRNA functional similarity.” IEEE Access 5 (2017): 2364-2372. [Link]

  • Buwen Cao, Shuguang Deng, Jiawei Luo*, Pingjian Ding, and Shulin Wang. “Identification of overlapping protein complexes by fuzzy K-medoids clustering algorithm in yeast protein-protein interaction networks.” Journal of Intelligent & Fuzzy Systems 34, no. 1 (2018): 93-103. [Link]

  • Jiawei Luo*, Pingjian Ding, Cheng Liang, Buwen Cao, and Xiangtao Chen. “Collective prediction of disease-associated miRNAs based on transduction learning.” IEEE/ACM transactions on computational biology and bioinformatics 14, no. 6 (2017): 1468-1475. [Link]

2016

  • Buwen Cao, Jiawei Luo*, Cheng Liang, Shulin Wang, and Pingjian Ding. “Pce-fr: A novel method for identifying overlapping protein complexes in weighted protein-protein interaction networks using pseudo-clique extension based on fuzzy relation.” IEEE transactions on nanobioscience 15, no. 7 (2016): 728-738. [Link]

  • Pingjian Ding, Jiawei Luo*, Qiu Xiao, and Xiangtao Chen. “A path-based measurement for human miRNA functional similarities using miRNA-disease associations.” Scientific Reports 6, no. 1 (2016): 32533. [Link]

  • Jiawei Luo*, Cong Huang, and Pingjian Ding. “A meta-path-based prediction method for human miRNA-target association.” BioMed Research International 2016 (2016). [Link]