5 citations · 13 across the 15 of their papers we have counts for
6 papers · 1 filter
Preserving Core Structures of Social Networks via Information Guided Multi-Step Graph Pruning
Yutong Hu, Bingxin Zhou, Jing Wang +2
Social networks often contain dense and overlapping connections that obscure their essential interaction patterns, making analysis and interpretation challenging. Identifying the s…
Fast and Interpretable Protein Substructure Alignment via Optimal Transport
Zhiyu Wang, Bingxin Zhou, Jing Wang +4
Proteins are essential biological macromolecules that execute life functions. Local structural motifs, such as active sites, are the most critical components for linking structure…
VenusX: Unlocking Fine-Grained Functional Understanding of Proteins
Yang Tan, Wenrui Gou, Bozitao Zhong +3
Deep learning models have driven significant progress in predicting protein function and interactions at the protein level. While these advancements have been invaluable for many b…
Sequence-Only Prediction of Binding Affinity Changes: A Robust and Interpretable Model for Antibody Engineering
Chen Liu, Mingchen Li, Yang Tan +3
A pivotal area of research in antibody engineering is to find effective modifications that enhance antibody-antigen binding affinity. Traditional wet-lab experiments assess mutants…
VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning
Yang Tan, Chen Liu, Jingyuan Gao +8
Natural language processing (NLP) has significantly influenced scientific domains beyond human language, including protein engineering, where pre-trained protein language models (P…
VenusMutHub: A systematic evaluation of protein mutation effect predictors on small-scale experimental data
Liang Zhang, Hua Pang, Chenghao Zhang +11
In protein engineering, while computational models are increasingly used to predict mutation effects, their evaluations primarily rely on high-throughput deep mutational scanning (…