[All publication list] [Research summary in one slide] [Google Scholar]
Selected publications(*equal contribution, #corresponding)
- Deep generative design of RNA aptamers using structural predictions.
F Wong, D He, A Krishnan, L Hong, A Wang, J Wang, Z Hu, S Omori, A Li, J Rao, Q Yu, W Jin, T Zhang, K Ilia, J Chen, S Zheng, I King, Y Li#, J Collins#. Nature Computational Science, 2024. [Full text] [GitHub]
- Fast, sensitive detection of protein homologs using deep dense retrieval.
L Hong*, Z Hu*, S Sun#, X Tang*, J Wang, Q Tan, L Zheng, S Wang, Sheng X, I King, M Gerstein#, Y Li#. Nature Biotechnology, 2024. [Full text] [GitHub]
[Nature Biotechnology Research Briefing] [CSE News]
[Nature Methods Research Highlights]
[Nature Portfolio WeChat Official Account (Chinese)]
[Nature Biotechnology WeChat Official Account (Chinese)]
[AIMShare WeChat Official Account (Technical details in Chinese)]
[AIMShare WeChat Official Account (Story behind the work in Chinese)]
[Invited talk at MBZUAI] [Invited talk at HKBU] [Invited talk at CCBSB2024] [Invited talk at PSI]
- USPNet: unbiased organism-agnostic and highly sensitive signal peptide predictor with deep protein language model.
J Shen, Q Yu, S Chen, Q Tan, J Li, Y Li#. Nature Computational Science, 2023. [Full text] [GitHub]
- The High-dimensional Space of Human Diseases Built from Diagnosis Records and Mapped to Genetic Loci.
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, X Gao#, A Rzhetsky#. Nature Computational Science, 2023. [Full text]
- Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis.
Y Chen*, Y Wang*, Y Chen, Y Cheng, Y Wei, Y Li, J Wang, Y Wei, TF Chan#, Y Li#. Nature Communications, 2022. [Full text]
[CSE News]
- HMD-ARG: hierarchical multi-task deep learning for annotating antibiotic resistance genes.
Y Li*, Z Xu*, W Han*, H Cao, R Umarov, A Yan, M Fan, H Chen, L Li, P Ho, X Gao. Microbiome, 2021. - RNA Secondary Structure Prediction By Learning Unrolled Algorithms.
X Chen*, Y Li*, R Umarov, X Gao, L Song. Eighth International Conference on Learning Representations (ICLR-20),
Oral (Accpetance rate=48/2599=1.85%)
[GaTech news] [Chinese news] [Chinese introduction] [Plain explanation] - DeepSimulator1.5: a more powerful, quicker and lighter simulator for Nanopore sequencing.
Y Li*, S Wang*, C Bi, Z Qiu, M Li, X Gao. Bioinformatics, 2020. [Code] [PDF] - A deep learning framework to predict binding preference of RNA constituents on protein surface.
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. Nature Communications, 2019.
[KAUST news] [Chinese introduction] [PDF] [Code] [Server]
- Estimating heritability and genetic correlations from large health datasets in the absence of genetic data.
G Jia, Y Li, H Zhang, I Chattopadhyay, A Jensen, D Blair, L Davis, P Robinson, T Dahlén, S Brunak, M Benson, G Edgren, N Cox, X Gao, A Rzhetsky. Nature Communications, 2019. [PDF]
[UChicago news] [Chinese introduction] - Deep learning in bioinformatics: introduction, application, and perspective in big data era.
Y Li, C Huang, L Ding, Z Li, Y Pan, X Gao. Methods, 2019. [PDF] [Code]
Cover article of the Methods issue: Deep Learning in Bioinformatics
Highly cited paper
- DeepSimulator: a deep simulator for nanopore sequencing.
Y Li, R Han, C Bi, M Li, S Wang, X Gao. Bioinformatics, 2018. [PDF] [Code] - DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy.
Y Li, F Xu, F Zhang, P Xu, M Fan, L Li, X Gao, R Han. Bioinformatics, 2018. [PDF] [Code] - DEEPre: sequence-based enzyme EC number prediction by deep learning.
Y Li, S Wang, R Umarov, B Xie, M Fan, L Li, X Gao. Bioinformatics, 2017. [PDF] [Server]