Author
Yang Li
National University of SingaporeORCID 0000-0003-2480-1972Unclaimed ORCID profile · follow to get notified of new preprints
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Biology Disciplines
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2021-2026 · peak 2/yrCo-Author Collaboration Network
10 top collaborators · 19 mutual co-author links6 papers
Recent·Most cited
- BioinformaticsbioRxivPreprint
ProteinConformers: large-scale and energetically profiled descriptions of protein conformational landscapes
Yihang Zhou, Chen Wei, Minghao Sun…·Yang Zhang
ProteinConformers provides a large-scale dataset of protein conformations with energy profiles and similarity annotations, offering a benchmarking framework and analysis platform.
DOI 10.64898/2026.02.20.707011 - BioinformaticsbioRxivPreprint
Ab initio RNA structure prediction with composite language model and denoised end-to-end learning
Yang Li, Chenjie Feng, Xi Zhang, Yang Zhang
DRfold2 improves RNA structure prediction using a composite language model and denoising module, outperforming existing methods in contact prediction and complementing AlphaFold3.
7 citations - BioinformaticsbioRxivPreprint
TCRfinder: Improved TCR virtual screening for novel antigenic peptides with tailored language models
Yang Li, Chaoting Zhang, Xi Zhang, Yang Zhang
TCRfinder improves TCR-peptide binding prediction using tailored language models, achieving higher accuracy than existing methods in identifying interacting TCRs and tumor neoantigens.
- BioinformaticsNature Computational SciencePeer-reviewed
Progressive and accurate assembly of multi-domain protein structures from cryo-EM density maps
Xiaogen Zhou, Yang Li, Chengxin Zhang…·Yang Zhang
This paper introduces DEMO-EM, a method for assembling multi-domain protein structures from cryo-EM maps, achieving high accuracy and outperforming existing methods.
75 citationstop 2% - BioinformaticsPLoS Computational BiologyPeer-reviewed
Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks
Yang Li, Chengxin Zhang, Eric W. Bell…·Yang Zhang
TripletRes predicts high-accuracy protein contact-maps from primary sequences using deep residual convolutional networks and coevolutionary matrices, outperforming existing methods on benchmark datasets.
85 citationstop 2%