1 citations · 2 across the 5 of their papers we have counts for
5 papers
Secondary Structure-Guided Novel Protein Sequence Generation with Latent Graph Diffusion
Yutong Hu, Yang Tan, Andi Han +3
The advent of deep learning has introduced efficient approaches for de novo protein sequence design, significantly improving success rates and reducing development costs compared t…
Protein Representation Learning with Sequence Information Embedding: Does it Always Lead to a Better Performance?
Yang Tan, Lirong Zheng, Bozitao Zhong +2
Deep learning has become a crucial tool in studying proteins. While the significance of modeling protein structure has been discussed extensively in the literature, amino acid type…
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…
A Unified View on Neural Message Passing with Opinion Dynamics for Social Networks
Outongyi Lv, Bingxin Zhou, Jing Wang +3
Social networks represent a common form of interconnected data frequently depicted as graphs within the domain of deep learning-based inference. These communities inherently form d…
Pro-PRIME: A general Temperature-Guided Language model to engineer enhanced Stability and Activity in Proteins
Fan Jiang, Mingchen Li, Jiajun Dong +23
Designing protein mutants of both high stability and activity is a critical yet challenging task in protein engineering. Here, we introduce PRIME, a deep learning model, which can…