7 citations · 12 across the 14 of their papers we have counts for
16 papers
E-OPSD: Taming Entropy Overshoot in On-Policy Self-Distillation
Yifei Liu, Minghao Fang, Xinyu Gu +6
On-policy self-distillation (OPSD) provides dense token-level supervision without a second model: one network acts as teacher with the reference solution and as student with only t…
Bison: Cross-Dataset Learning for Unseen-Compound Perturbation Prediction
Yunfan Liu, Kasra Ghorbani, Yufei Huang +6
Predicting transcriptional responses to unseen compounds is limited by fragmented chemical coverage and heterogeneous experimental platforms and gene panels. To assess molecular ge…
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…
Multimodal Regression for Enzyme Turnover Rates Prediction
Bozhen Hu, Cheng Tan, Siyuan Li +4
The enzyme turnover rate is a fundamental parameter in enzyme kinetics, reflecting the catalytic efficiency of enzymes. However, enzyme turnover rates remain scarce across most org…
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…
VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling
Siyuan Li, Zedong Wang, Zicheng Liu +5
Similar to natural language models, pre-trained genome language models are proposed to capture the underlying intricacies within genomes with unsupervised sequence modeling. They h…
Advances of Deep Learning in Protein Science: A Comprehensive Survey
Bozhen Hu, Cheng Tan, Lirong Wu +7
Protein representation learning plays a crucial role in understanding the structure and function of proteins, which are essential biomolecules involved in various biological proces…
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…
Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction
Yufei Huang, Siyuan Li, Jin Su +9
Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimati…
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…