个人简历

刘悦，博士，1994年生。2023年获湖南大学计算机科学与技术博士学位，期间于2021–2022年在新加坡南洋理工大学计算机科学系公派留学；2023–2025年在西湖大学生命科学学院从事博士后研究工作。

主要研究方向为机器学习与深度学习在基因调控与基因组功能解析中的应用，聚焦于基因组序列驱动的预测模型及其在生物医学中的应用。主持国家留学基金委公派留学项目，并参与多项国家自然科学基金面上项目。以第一作者在Briefings in Bioinformatics、IEEE/ACM Transactions on Computational Biology and Bioinformatics、Neurocomputing及IEEE BIBM等期刊或会议发表论文多篇。

研究方向

人工智能，大语言模型，生物信息学，计算生物学。

主要承担的教学课程

本科生课程：语言程序设计

代表性学术成果

1. Liu Y, Zhang J, Wang S, et al. Are dropout imputation methods for scRNA-seq effective for scATAC-seq data? [J]. Briefings in Bioinformatics, 2022, 23(1): 1-12. (SCI 2区)

2. Liu Y, Wang S L, Zhang J F, et al. A neural collaborative filtering method for identifying miRNA-disease associations[J]. Neurocomputing, 2021, 422: 176-185. (SCI 2区)

3. Liu Y, Wang S L, Zhang J F, et al. DMFMDA: prediction of microbe-disease associations based on deep matrix factorization using Bayesian personalized ranking[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2020, 18(5): 1763-1772. (SCI 3区，CCF B类)

4. Liu Y, Wang S L, Zhang J F. Prediction of microbe–disease associations by graph regularized non-negative matrix factorization[J]. Journal of Computational Biology, 2018, 25(12): 1385-1394. (SCI)

5. Liu Y, Zhang J, Wang S, et al. A heterogeneous graph cross-omics attention model for single-cell representation learning[C]. 2022 IEEE international conference on bioinformatics and biomedicine (BIBM), 2022: 270-275. (CCF B类会议)

6. Liu Y, Wang S L, Zhang J F, et al. LncRNA-disease associations prediction based on neural network-based matrix factorization[J]. IEEE Access, 2020, 11: 59071-59080. (SCI 3区)

7. Liu Y, Wang S L. A Novel Approach for Predicting Microbe-Disease Associations by Structural Perturbation Method[C]//International Conference on Intelligent Computing. Cham: Springer International Publishing, 2021: 211-221. (CCF C类会议)

联系方式

Email：03453@zjhu.edu.cn