研究成果概述：

研究领域聚焦于AI模型及其在生物药研发领域的创新应用。以一作、通讯身份在多个Nature子刊、PNAS、Genome Biology、PLOS Computational Biology等顶级期刊会议发表学术论文20余篇。目前特别专注于AI大模型与基于RNA的超高通量并行实验方向。具体如下：

(1)基于AI与单细胞RNA技术的靶点发现与高通量验证

(2)核酸药物的序列修饰、递送系统设计与高通量筛选

(3)蛋白质的AI从头设计与高通量优化

工作经历：

(1)2024-06至今，中国科学院过程所，生物药制备与递送全国重点实验室（原生化工程国重）副主任

(1)2020-12至2024-05，上海交通大学，电子信息与电气工程学院，副教授

(2)2017-10至2020-12，卡内基梅隆大学，机器学习系，博后，导师：Ziv Bar-Joseph

(3)2017-07至2017-09，百度在线网络技术（北京）有限公司，大数据实验室，高级研发工程师

学历：

(1)2012-09至2017-07，清华大学，自动化系，工学博士，导师：李衍达院士、汪小我教授

(2)2016-01至2016-04，斯坦福大学，统计系，访问学生，导师：Wing Hung Wong院士

(3)2008-09至2012-07，西安交通大学，自动化系，工学学士

项目情况：

(1)2025年，中国科学院院引才计划B类入选者

(2)2024年，中国科学院过程工程研究所引才计划B类入选者

(3)2023年，国家重点研发计划BT与IT融合重点专项课题负责人

(4)2023年，上海东方英才

(5)2023年，上海交通大学科技成果转化项目负责人

(6)2023年，共青团中央“创青春”大赛项目全国金奖（核酸药物设计）负责人

(7)2023年，上海交通大学“交大之星”计划合作负责人

(8)2022年，国家自然科学基金青年基金

(9)2022年，上海市海外高层次人才计划

(10)2021年，上海市浦江人才计划

(11)2018年，Reconstructing regulatory networks from time series single cell data，美国NIH，参与

(12)2017年，Comprehensive, §exible and FAIR Tools for the HuBMAP HIVE，美国NIH，参与

当前担任学会和期刊相关任职情况：

(1)中国自动化学会智能健康与生物信息专业委员会委员

(2)中国计算机学会生物信息学专委会委员

(3)中国人工智能学会生物信息与人工生命专业委员会委员

(4)Quantitative Biology期刊，Associate editor

(5)Nature Methods、Nature Computational Science、Nature Machine Intelligence、Nature Communications、Cell systems等期刊审稿专家

1.Jiachen L., Xiaoyong P., Yuan, Y.# & Hong-bin S#. TFvelo: gene regulation inspired RNA velocity estimation, Nature Communications, 2024, 15(1): 1387. 

2.Jiachen L., Siheng C., Xiaoyong P., Yuan, Y.# & Hong-bin S#. (2022). Cell clustering for spatial transcriptomics data with graph neural network. Nature Computational Science, 10.1038/s43588-022-00266-5. 

3.Yuan, Y., & Bar-Joseph, Z. (2019). Deep learning for inferring gene relationships from single-cell expression data. PNAS, 116(52), 27151-27158. 

4.Yuan, Y.*, Liu, B.*, Xie, P., Zhang, M. Q., Li, Y., Xie, Z.#, & Wang, X.# (2015). Model-guided quantitative analysis of microRNA-mediated regulation on competing endogenous RNAs using a synthetic gene circuit. PNAS, 112(10), 3158-3163. 

5.Yuan, Y., & Bar-Joseph, Z. (2020). GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data. Genome Biology, 21(1), 1-16.

6.Yuan Y., Cosme C Jr, Adams TS, Schupp J, Sakamoto K, Xylourgidis N, ... & Bar-Joseph, Z. (2022) CINS: Cell Interaction Network inference from Single cell expression data. PLoS Comput Biol 18(9): e1010468.

7.J Du, YC Yang, ZJ An, MH Zhang, XH Fu, ZF Huang#, Y Yuan#, J Hou#. (2023). Advances in spatial transcriptomics and related data analysis strategies. Journal of Translational Medicine, 21 (1), 1-21.

8.Yuan, Y., & Bar-Joseph, Z. (2021). Deep learning of gene relationships from single cell time-course expression data, Briefings in Bioinformatics, 22(5).

9.Kaiyuan Yang, Jiabei Cheng, Shenghao Cao, Xiaoyong Pan, Hong-Bin Shen, Ye Yuan#. (2025). Predicting transcriptional changes induced by molecules with MiTCP, Briefings in Bioinformatics.

10.Jiabei Cheng, Xiaoyong Pan, Yi Fang, Kaiyuan Yang, Yiming Xue, Qingran Yan, Ye Yuan#. (2024). GexMolGen: cross-modal generation of hit-like molecules via large language model encoding of gene expression signatures, Briefings in Bioinformatics.

11.Yuan, Y., Qushuo Chen, Jun Mao, Guipeng Li, Xiaoyong Pan. DG-Affinity: predicting antigen–antibody affinity with language models from sequences. (2023). BMC Bioinformatics. 1471-2105.

12.Yuan, Y., Yuqi Wu, Jiabei Cheng, Kaiyuan Yang, Yilin Xia, Hongguang Wu, Xiaoyong Pan. (2023). Applications of Artificial Intelligence to LNP Delivery. Particuology. 2210-4291.

13.Yuan, Y.*, Ren, X.*, Xie, Z., & Wang, X. (2016). A quantitative understanding of microRNA-mediated competing endogenous RNA regulation. Quantitative Biology, 4(1), 47-57.

14.Yuan, Y., Liu, B., Xie, P., Zhang, M. Q., Li, Y., Xie, Z., & Wang, X. Quantitative analysis of microRNA mediated regulation on competing endogenous RNAs, 2016 Intelligent Systems for Molecular Biology (ISMB), Orlando, Florida. 

15.Wei, L.*, Yuan, Y.*, Hu, T., Li, S., Cheng, T., Lei, J., ... & Wang, X. (2019). Regulation by competition: a hidden layer of gene regulatory network. Quantitative Biology, 7(2), 110-121. 

16.Yuxuan Wang, Ying Xia, Junchi Yan, Ye Yuan, Hong-Bin Shen. (2023). ZeroBind: A protein-specific zero-shot predictor with subgraph matching for drug-target interactions. Nature Communications.

17.Yin H, Distler O, Shen L, Xu X, Yuan Y, Li R, Liu B, Li Q, Huang Q, Xie F, Zhang Z, Liang R, Dai X, Chen X, Li B, Yan Q, Lu L. (2023). Endothelial response to type I interferon contributes to vasculopathy and fibrosis and predicts disease progression of systemic sclerosis. Arthritis Rheumatol. doi: 10.1002/art.42662. Epub ahead of print. PMID: 37488975.

18.Haotian T., Yuan, Y., & Bar-Joseph, Z. (2022). Clustering spatial transcriptomics data. Bioinformatics, 38(4), 997-1004.

19.Wei, L., Yuan, Y., & Wang, X. (2017). Challenges in large-scale synthetic gene circuits design. Sheng wu gong cheng xue bao= Chinese journal of biotechnology, 33(3), 372-385.

20.J Ren, Y Zhang, W Guo, K Feng, Y Yuan, T Huang, YD Cai. (2023). Identification of genes associated with the impairment of olfactory and gustatory functions in COVID-19 via machine-learning methods. Life, 13 (3), 798.

21.Bin Liu*; Ye Yuan#*; Xiaoyong Pan; Hongbin Shen; Cheng Jin#; AttSiOff: a self-attention based approach on siRNA design with inhibition and off-target effect prediction, Med-X, 2024. 

22.Ye Yuan*#, Yang Chen*, Rui Liu*, Gula Que, Yina Yuan, Guipeng Li#; A de novo AI-designed adenine base editor, Genome Biology, under revision, 2025. 

23.Shenghao Cao, Ye Yuan#; stFormer, a foundation model for spatial transcriptomics, preparing, 2025.

24.Yuqi Wu, Ye Yuan#; KDMSi: a deep-learning based inhibition rate predictor for chemically modified siRNAs; preparing, 2025.