5 papers
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…
An extensive simulation study evaluating the interaction of resampling techniques across multiple causal discovery contexts
Ritwick Banerjee, Bryan Andrews, Erich Kummerfeld
Despite the accelerating presence of exploratory causal analysis in modern science and medicine, the available non-experimental methods for validating causal models are not well ch…
A PLMs based protein retrieval framework
Yuxuan Wu, Xiao Yi, Yang Tan +3
Protein retrieval, which targets the deconstruction of the relationship between sequences, structures and functions, empowers the advancing of biology. Basic Local Alignment Search…
CPE-Pro: A Structure-Sensitive Deep Learning Method for Protein Representation and Origin Evaluation
Wenrui Gou, Wenhui Ge, Yang Tan +3
Protein structures are important for understanding their functions and interactions. Currently, many protein structure prediction methods are enriching the structure database. Disc…
Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language Models
Yang Tan, Mingchen Li, Bingxin Zhou +7
Fine-tuning Pre-trained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised lea…