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综合介绍 General Introduction 易海成，博士，西北工业大学副教授，博士毕业于中国科学院大学，专业计算机应用技术。2021年至2022年于新加坡南洋理工大学 Nanyang Technological University (NTU) 计算机科学与工程学院进行访问。研究面向人工智能与生命健康前沿交叉领域，方向包括模式识别与数据挖掘、知识图谱、几何机器学习、推荐系统、表征和学习理论等。参与国家重点研发计划，国家自然科学基金杰青/优青、面上项目，国家高层次人才计划项目等科研项目。在Cell子刊、中科院一区期刊，IEEE/ACM汇刊及中国计算机学会推荐期刊和会议上发表论文40余篇。相关工作得到包括美国医学与生物工程院院士在内的国内外专家学者的高度评价，科研成果多次得到中国科学院官网，光明网，人民网等报道。曾获2021年“中国科学院院长优秀奖”，奖励在科学研究和技术创新方面做出突出成绩。I am an Associate Professor at the School of Computer Sciecne, Northwestern Polytechnical University, Xi'an China. I pursued my Ph.D. in Computer Science at the University of Chinese Academy of Sciences from 2017 to 2022, under the supervision of Prof. Zhuhong You. From 2021 to 2022, I am a visiting scholar at Nanyang Technological University (NTU) in Singapore, supervised by Prof. Kwoh Chee Keong.From Big Data to Big Mind, my reserch focuses on building data-driven, advanced artificial intelligence frameworks for solving cutting-edge issues in biomedicine and healthcare systems, where I have contributed more than 40 papers in journals and conferences like iScience, IEEE Trans., Brief. Bioinform., and IEEE BIBM. Some work was reported by the official website of the Chinese Academy of Sciences.My current research topics include Geometric/Graph Learning, Multimodal Learning, Knowledge Graphs, Generative Drug Design, Molecular Representation Learning, and welcome all potential collaborations.  M.S. and Ph.D. positions are now open for self-motivated students, interested students please drop me an email with a detailed CV.招收24年研究生1名！欢迎有志从事科学研究的同学联系！ 个人相册

教育教学

教育教学 Education and teaching 教育信息 U10G13011.05《程序设计基础 (C) 》，依托在线课程资源和团队自研教学平台，实施KTCPD线上线下混合式教学；U10G11017.02《智能时代的计算机科学》，2023级计算机类新生研讨课；U10M12039S.01《人工智能数据分析》，暑期国际学堂。

荣誉获奖

科学研究 Scientific Research [04] J. Zheng, H.-C. Yi (易海成)*通讯作者, et al. “Equivariant 3D-conditional Diffusion Model for De novo Drug Generation”, IEEE Transactions on Neural Networks and Learning Systems, under review. 2024.[03] S. Chen, H.-C. Yi (易海成)*通讯作者, et al. “Local-global Structure-aware Geometric Equivariant Graph Representation Learning for Predicting Protein-Ligand Binding Affinity”, IEEE Transactions on Neural Networks and Learning Systems, under review. 2024.[02] L.-X. Hou, H.-C. Yi (易海成)*通讯作者, et al. “Predicting Accurate Drug-Drug Interaction Events with Heterogeneous Attribute Graph Learning”, Artificial Intelligence in Medicine, under review. 2024.[01] S. He, L. Yun, H.-C. Yi (易海成)*通讯作者, "Fusing graph transformer with multi-aggregate GCN for enhanced drug–disease associations prediction," BMC Bioinformatics, vol. 25, no. 1, pp. 1-18, 2024.[00] X. Su, P. Hu, H.-C. Yi (易海成), Z. You, Lun Hu, "Predicting Drug-Target Interactions Over Heterogeneous Information Network," IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 1, pp. 562-572, 2023.[1] H.-C. Yi, Z.-H. You, D.-S. Huang, and C. K. Kwoh, "Graph representation learning in bioinformatics: trends, methods and applications," Briefings in Bioinformatics, vol 23, no. 1, bbab340, 2022.[2] H.-C. Yi, Z.-H. You, D.-S. Huang, Z.-H. Guo, K. C. Chan, and Y. Li, "Learning Representations to Predict Intermolecular Interactions on Large-Scale Heterogeneous Molecular Association Network," iScience, vol. 23, no. 7, p. 101261, 2020.[3] H.-C. Yi, Z.-H. You, Z.-H. Guo, D.-S. Huang, and K. C. Chan, "Learning representation of molecules in association network for predicting intermolecular associations," IEEE/ACM transactions on computational biology and bioinformatics, vol. 18, no. 6, pp. 2546-2554, 2021.[4] H.-C. Yi, Z.-H. You, L. Wang, X.-R. Su, X. Zhou, and T.-H. Jiang, "In silico drug repositioning using deep learning and comprehensive similarity measures," BMC Bioinformatics, vol. 22, no. 3, pp. 1-15, 2021.[5] H.-C. Yi, Z.-H. You, M.-N. Wang, Z.-H. Guo, Y.-B. Wang, and J.-R. Zhou, "RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information," BMC Bioinformatics, vol. 21, no. 1, pp. 1-10, 2020.[6] H.-C. Yi, Z.-H. You, X.-R. Su, D.-S. Huang, and Z.-H. Guo, "A Unified Deep Biological Sequence Representation Learning with Pretrained Encoder-Decoder Model," in International Conference on Intelligent Computing, 2020, pp. 339-347: Springer, Cham.[7] H.-C. Yi et al., "Learning distributed representations of RNA and protein sequences and its application for predicting lncRNA-protein interactions," Computational and structural biotechnology journal, vol. 18, pp. 20-26, 2020.[8] H.-C. Yi, et al., "ACP-DL: a deep learning long short-term memory model to predict anticancer peptides using high-efficiency feature representation," Molecular Therapy-Nucleic Acids, vol. 17, pp. 1-9, 2019.[9] H.-C. Yi, Z.-H. You, Y.-B. Wang, Z.-H. Chen, Z.-H. Guo, and H.-J. Zhu, "In Silico identification of anticancer peptides with stacking heterogeneous ensemble learning model and sequence information," in International Conference on Intelligent Computing, 2019, pp. 313-323: Springer, Cham.[10] H.-C. Yi, Z.-H. You, and Z.-H. Guo, "Construction and analysis of molecular association network by combining behavior representation and node attributes," Frontiers in genetics, vol. 10, p. 1106, 2019.[11] H.-C. Yi, Z.-H. You, D.-S. Huang, X. Li, T.-H. Jiang, and L.-P. Li, "A deep learning framework for robust and accurate prediction of ncRNA-protein interactions using evolutionary information," Molecular Therapy-Nucleic Acids, vol. 11, pp. 337-344, 2018.[12] Z.-H. Guo, Z.-H. You, D.-S. Huang, H.-C. Yi, K. Zheng, Z.-H. Chen, and Y.-B. Wang, "MeSHHeading2vec: a new method for representing MeSH headings as vectors based on graph embedding algorithm," Briefings in bioinformatics, vol. 22, no. 2, pp. 2085-2095, 2021.[13] Y.-B. Wang, Z.-H. You, S. Yang, H.-C. Yi, Z.-H. Chen, and K. Zheng, "A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network," BMC medical informatics and decision making, vol. 20, no. 2, pp. 1-9, 2020.[14] X. Su, Z. You, and H.-C. Yi, "Prediction of LncRNA-Disease Associations Based on Network Representation Learning," in 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020, pp. 1805-1812: IEEE.[15] J. Li, X Shi, Z.-H. You, H.-C. Yi, Z. Chen, Q. Lin, M. Fang, "Using weighted extreme learning machine combined with scale-invariant feature transform to predict protein-protein interactions from protein evolutionary information," IEEE/ACM transactions on computational biology and bioinformatics, vol. 17, no. 5, pp. 1546-1554, 2020.

科学研究

学术活动 Professional Activities 学术服务期刊编辑 Editorship:BMC Bioinformatics: Editorial Board MemberFrontiers in Bioinformatics: Review Editor分会主席 Session Chair：2019 International Conference on Intelligent Computing, August 3-6, 2019, Nanchang, China.2020 International Conference on Intelligent Computing, October 2-5, 2020, Bari, Italy.2023 International Conference on Intelligent Computing, August 10-13, 2023, Zhengzhou, China.程序委员会 Program Committee：IEEE International Conference on Bioinformatics & Biomedicine (BIBM'22), Las Vegas, NV, USA, December 6-9, 2022IEEE International Conference on Bioinformatics & Biomedicine (BIBM'23), Istanbul, Turkey, December 5-8, 2023The 3rd Workshop on Graph Learning Benchmarks (GLB 2023), conjunction with the KDD 2023, Long Beach, CA, USA, August 7, 2023IEEE International Conference on Bioinformatics & Biomedicine (BIBM'24), Lisbon, Portugal, December 3-6, 2024审稿人 Reviewer：IEEE TKDE, Cell Patterns, Briefings in Bioinformatics, Engineering Applications of Artificial Intelligence, IEEE/ACM TCBB, BMC Bioinformatics, Interdisciplinary Sciences: Computational Life Sciences, Current Bioinformatics, Learning on Graphs Conference (LoG 2022, 2023)

学术成果

综合介绍