Xuan Lin (林 轩) Lecturer School of Computer Science Xiangtan University Location: Engineering Building, #401, Yuhu District, Xiangtan, Hunan, China About Me | Work Experience | Research Interests | Education | Publications | Grants | Professional Services | Email: jack_lin@xtu.edu.cn [中文主页] [Google Scholar] [GitHub]

About Me

I am currently a lecturer in the School of Computer Science, Xiangtan University (XTU), P. R. China. Prior to joining XTU, I received my Ph.D. degree in School of Computer Science and Electronic Engineering from Hunan University in June 2021. Since October 2019 till August 2020, I was a visiting scholar at Univesity of Illinois at Chicago (UIC), under the supervison of Prof. Philip S. Yu. My research interests include machine learning and graph neural networks for bioinformatics. I have published more than 10 technical papers in refereed conference proceedings such as AAAI, IJCAI, ECAI, and BIBM, and journals such as IEEE TKDE, TCBB, WIREs Computational Molecular Science and Briefings in Bioinformatics.

Work Experience

• 2021.7-Now        Lecturer, School of Computer Science, Xiangtan University

Research Interests

My research interests include machine learning and graph neural networks for bioinformatics. Currently, I focus on the following research topics:

• Molecular property prediction
• Compound-protein interaction prediction
• Drug-target binding affinity prediction
• Drug-drug interaction prediction

Education

• 2017.09-2021.06        Ph.D. from School of Computer Science and Electronic Engineering, Hunan University.        Supervisor: Prof. Zhe Quan
• 2019.10-2020.08        Visiting Ph.D. from Department of Computer Science, University of Illinois at Chicago.        Supervisor: Prof. Philip S. Yu

Publications

• Xuan Lin, Qi Wen, Sijie Yang, Zu-Guo Yu, Yahui Long*, and Xiangxiang Zeng, “Interpretable attention network with multi-view learning for drug-drug interaction prediction,” 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2023, accepted. [PDF] [Code]
• Wen Tao, Yuansheng Liu*, Xuan Lin, and Xiangxiang Zeng*, “Dynamic hypergraph contrastive learning for multi-relational drug-gene interaction prediction,” Briefings in Bioinformatics, 2023, accepted.
• Xuan Lin, Lichang Dai, Yafang Zhou, Zu-Guo Yu, Wen Zhang, Jian-Yu Shi, Dong-Sheng Cao, Li Zeng, Haowen Chen*, Bosheng Song*, Philip S. Yu and Xiangxiang Zeng, “Comprehensive evaluation of deep and graph learning on drug–drug interactions prediction,” Briefings in Bioinformatics, 24(4): bbad235, 2023. [PDF] [Code]
• Xuan Lin, Zhe Quan*, Zhi-Jie Wang, Yan Guo, Xiagxiang Zeng, Philip S Yu, “Effectively Identifying Compound-Protein Interaction using Graph Neural Representation,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022, accepted. [PDF] [Code]
• Tengfei Ma, Xuan Lin*, Bosheng Song, Philip S Yu, Xiagxiang Zeng*, “KG-MTL: Knowledge Graph Enhanced Multi-Task Learning for Molecular Interaction,” IEEE Transactions on Knowledge and Data Engineering, 2022, accepted. [PDF] [Code]
• Xiaoqin Pan, Xuan Lin*, Dongsheng Cao, Xiagxiang Zeng*, Philip S Yu, Lifang He, Ruth Nussinov, Feixiong Cheng, “Deep learning for drug repurposing: methods, databases, and applications,” WIREs Computational Molecular Science, 2022, accepted. [PDF], Highly Cited Paper
• Bosheng Song, Zimeng Li, Xuan Lin, Jianmin Wang, Tian Wang, Xiangzheng Fu*, “Pretraining model for biological sequence data,” Briefings in Functional Genomics, 20(3), 181-195, 2021. [PDF]
• Kuan Li, Yue Zhong*, Xuan Lin*, Zhe Quan, “Predicting the disease risk of protein mutation sequences with pre-training model,” Frontiers in Genetics, 11, 1-10, 2020. [PDF] [Bibtex]
• Xuan Lin, Zhe Quan, Zhi-Jie Wang*, Huang Huang, Xiangxiang Zeng, “A novel molecular representation with BiGRU neural networks for learning atom,” Briefings in Bioinformatics, 21 (6), 2099-2111, 2020. [PDF] [Bibtex]
• Xuan Lin, Zhe Quan*, Zhi-Jie Wang*, Tengfei Yu, Ma, Xiangxiang Zeng, “KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction,” The 29th International Joint Conference on Artifical Intelligence (IJCAI), 2739-2745, 2020. [PDF] [Bibtex] [Poster] [Code]
• Xuan Lin, Kaiqi Zhao, Tong Xiao, Zhe Quan*, Zhi-Jie Wang*, Philip S Yu, “DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction,” The 24th European Conference on Artificial Intelligence (ECAI), 1-8, 2020. [PDF] [Bibtex] [Code]
• Jian Yin, Chunjing Gan, Kaiqi Zhao, Xuan Lin, Zhe Quan, Zhi-Jie Wang*, “A Novel Model for Imbalanced Data Classification,” The 34th AAAI Conference on Artifical Intelligence (AAAI), 95-104, 2020. [PDF] [Bibtex]
• Zhe Quan, Yan Guo, Xuan Lin, Zhi-Jie Wang*, Xiangxiang Zeng, “GraphCPI: Graph Neural Representation Learning for Compound-Protein Interaction,” 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 717-722, 2019. [PDF] [Bibtex]
• Zhe Quan, Xuan Lin, Zhi-Jie Wang*, Yan Liu, Fan Wang, Kenli Li, “A System for Learning Atoms Based on Long Short-Term Memory Recurrent Neural Networks,” 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 728-733, 2018. [PDF] [Bibtex]

Grants

• National Natural Science Foundation of China Youth Program, “Research on multimodal graph deep learning model for drug repurposing prediction”, Grant No. 62202413, 2023.01-2025.12.

Professional Services

Conference Program Committee:

• The International Joint Conference on Artificial Intelligence (IJCAI 2020-2023)
• The AAAI Conference on Artificial Intelligence (AAAI 2021-2022)
• International Conference on High Performance Computing and Communications (HPCC 2019)

Invited Journal Reviewer:

• Briefings in Bioinformatics
• Neurocomputing
• Neural Networks
• Frontiers in Genetics
• International Journal of Pattern Recognition and Artificial Intelligence

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