2 citations · 6 across the 12 of their papers we have counts for
12 papers
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…
TCRTransBench: A Comprehensive Benchmark for Bidirectional TCR-Peptide Sequence Generation
Yiming Wang, Weiyu Xiao, Jiangbin Zheng +1
T-cell receptor (TCR) interactions with antigenic peptides underpin adaptive immunity and are pivotal for personalized immunotherapy and vaccine development. Despite recent progres…
Departures: Distributional Transport for Single-Cell Perturbation Prediction with Neural Schrödinger Bridges
Changxi Chi, Yufei Huang, Jun Xia +4
Predicting single-cell perturbation outcomes directly advances gene function analysis and facilitates drug candidate selection, making it a key driver of both basic and translation…
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…
MetaEnzyme: Meta Pan-Enzyme Learning for Task-Adaptive Redesign
Jiangbin Zheng, Han Zhang, Qianqing Xu +2
Enzyme design plays a crucial role in both industrial production and biology. However, this field faces challenges due to the lack of comprehensive benchmarks and the complexity of…
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…
Graph-level Protein Representation Learning by Structure Knowledge Refinement
Ge Wang, Zelin Zang, Jiangbin Zheng +2
This paper focuses on learning representation on the whole graph level in an unsupervised manner. Learning graph-level representation plays an important role in a variety of real-w…
Progressive Multi-Modality Learning for Inverse Protein Folding
Jiangbin Zheng, Stan Z. Li
While deep generative models show promise for learning inverse protein folding directly from data, the lack of publicly available structure-sequence pairings limits their generaliz…
Co-modeling the Sequential and Graphical Routes for Peptide Representation Learning
Zihan Liu, Ge Wang, Jiaqi Wang +2
Peptides are formed by the dehydration condensation of multiple amino acids. The primary structure of a peptide can be represented either as an amino acid sequence or as a molecula…
Deciphering RNA Secondary Structure Prediction: A Probabilistic K-Rook Matching Perspective
Cheng Tan, Zhangyang Gao, Hanqun Cao +6
The secondary structure of ribonucleic acid (RNA) is more stable and accessible in the cell than its tertiary structure, making it essential for functional prediction. Although dee…