Biography
Yu Li works at the intersection between machine learning, healthcare, and bioinformatics. He has published 70 papers in the top venues, such as Nature Biotechnology and Nature Computational Science. Since he joined CUHK 3.5 years ago, his group has four works published in Nature Biotechnology, Nature Communications and Nature Computational Science with him as the corresponding author or co-first author. His work on antibiotic resistance gene detection has been featured by Forbes 30 Under 30 Asia list, Class of 2022. His team won the Championship in the CASP15-RNA 3D structure prediction competition in 2022. He also received the Department Exemplary Teaching Awards in 2024 at the CSE department of CUHK.
He got his Bachelor degree (first-class honorary) in Biosciences from the BEI Shizhang Elite Class at USTC in 2015. He got Master degree in Computer Science at KAUST in December 2016 and his Ph.D. in Computer Science from KAUST in October 2020. He is the first Chinese commencement student speaker candidate in the KAUST history. He was born in 1995.
- L Hong, Z Hu, S Sun, X Tang, J Wang, Q Tan, L Zheng, S Wang, Sheng X, I King, M Gerstein$, Y Li$. (2024). “Fast, sensitive detection of protein homologs using deep dense retrieval.” Nature Biotechnology (IF=46.9). [corresponding author, author 12/12]
- J Shen, Q Yu, S Chen, Q Tan, J Li, Y Li$. (2023). “USPNet: unbiased organism-agnostic and highly sensitive signal peptide predictor with deep protein language model”. Nature Computational Science (IF=11.3). [corresponding author, author 6/6]
- G Jia*, Y Li*, X Zhong, K Wang, M Pividori, R Alomairy, A Esposito, H Ltaief, C Terao, M Akiyama, K Matsuda, D Keyes, H Im, T Gojobori, Y Kamatani, M Kubo, N Cox, J Evans, X Gao, A Rzhetsky. (2023). “The continuous space of human diseases mapped to genetic loci predicts disease trajectories and risk”. Nature Computational Science (IF=11.3). [author 2/20, co-first]
- Y Chen*, Y Wang*, Y Chen, Y Cheng, Y Wei, Y Li, J Wang, Y Wei, TF Chan$, Y Li$. (2022). “Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis.” Nature Communications (IF= 16.6). [corresponding author, author 10/10].
- Y Li, Z Xu, W Han, H Cao, R Umarov, A Yan, M Fan, H Chen, L Li, P Ho, X Gao. (2021). “HMD-ARG: hierarchical multi-task deep learning for annotating antibiotic resistance genes.” Microbiome (IF= 15.5). [author 1/11].
- J Lam*, Y Li*, L Zhu, R Umarov, H Jiang, A Heliou, F Sheong, T Liu, Y Long, Y Li, L Fang, R Altman, W Chen, X Huang, X Gao. (2019). “A deep learning framework to predict binding preference of RNA constituents on protein surface.” Nature Communications (IF= 16.6). Recommended by F1000Prime. [co-first author, author 2/15]