2 citations · 6 across the 12 of their papers we have counts for
5 papers · 1 filter
MetaGEM: Bottom-Up Reconstruction of Genome-Scale Metabolic Networks via Deep Enzyme-Metabolite Anchoring
Weiyu Xiao, Jiangbin Zheng, Stan Z. Li
Genome-scale metabolic models (GEMs) are essential tools for systems biology and rational chassis design, but conventional top-down reconstruction depends heavily on sequence homol…
DapPep: Domain Adaptive Peptide-agnostic Learning for Universal T-cell Receptor-antigen Binding Affinity Prediction
Jiangbin Zheng, Qianhui Xu, Ruichen Xia +1
Identifying T-cell receptors (TCRs) that interact with antigenic peptides provides the technical basis for developing vaccines and immunotherapies. The emergent deep learning metho…
Pan-protein Design Learning Enables Task-adaptive Generalization for Low-resource Enzyme Design
Jiangbin Zheng, Ge Wang, Han Zhang +1
Computational protein design (CPD) offers transformative potential for bioengineering, but current deep CPD models, focused on universal domains, struggle with function-specific de…
NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics
Jingbo Zhou, Shaorong Chen, Jun Xia +6
Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the high-throughput analysis of protein composition in biological tissues. Many deep learning m…
AdaNovo: Adaptive \emph{De Novo} Peptide Sequencing with Conditional Mutual Information
Jun Xia, Shaorong Chen, Jingbo Zhou +4
Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the analysis of protein composition in biological samples. Despite the development of various d…