67 citations · 220 across the 37 of their papers we have counts for
14 papers · 1 filter
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…
AlphaFold Database Debiasing for Robust Inverse Folding
Cheng Tan, Zhenxiao Cao, Zhangyang Gao +3
The AlphaFold Protein Structure Database (AFDB) offers unparalleled structural coverage at near-experimental accuracy, positioning it as a valuable resource for data-driven protein…
G2PDiffusion: Cross-Species Genotype-to-Phenotype Prediction via Evolutionary Diffusion
Mengdi Liu, Zhangyang Gao, Hong Chang +3
Understanding how genes influence phenotype across species is a fundamental challenge in genetic engineering, which will facilitate advances in various fields such as crop breeding…
MeToken: Uniform Micro-environment Token Boosts Post-Translational Modification Prediction
Cheng Tan, Zhenxiao Cao, Zhangyang Gao +6
Post-translational modifications (PTMs) profoundly expand the complexity and functionality of the proteome, regulating protein attributes and interactions that are crucial for biol…
FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning
Sizhe Liu, Jun Xia, Lecheng Zhang +8
Molecular relational learning (MRL) is crucial for understanding the interaction behaviors between molecular pairs, a critical aspect of drug discovery and development. However, th…
A Teacher-Free Graph Knowledge Distillation Framework with Dual Self-Distillation
Lirong Wu, Haitao Lin, Zhangyang Gao +2
Recent years have witnessed great success in handling graph-related tasks with Graph Neural Networks (GNNs). Despite their great academic success, Multi-Layer Perceptrons (MLPs) re…
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks
Tianyu Fan, Lirong Wu, Yufei Huang +4
Recent years have witnessed the great success of graph pre-training for graph representation learning. With hundreds of graph pre-training tasks proposed, integrating knowledge acq…
MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding
Lirong Wu, Yijun Tian, Yufei Huang +4
Protein-Protein Interactions (PPIs) are fundamental in various biological processes and play a key role in life activities. The growing demand and cost of experimental PPI assays r…
Switch EMA: A Free Lunch for Better Flatness and Sharpness
Siyuan Li, Zicheng Liu, Juanxi Tian +9
Exponential Moving Average (EMA) is a widely used weight averaging (WA) regularization to learn flat optima for better generalizations without extra cost in deep neural network (DN…
A Graph is Worth Words: Euclideanizing Graph using Pure Transformer
Zhangyang Gao, Daize Dong, Cheng Tan +3
Can we model Non-Euclidean graphs as pure language or even Euclidean vectors while retaining their inherent information? The Non-Euclidean property have posed a long term challenge…
General Point Model with Autoencoding and Autoregressive
Zhe Li, Zhangyang Gao, Cheng Tan +2
The pre-training architectures of large language models encompass various types, including autoencoding models, autoregressive models, and encoder-decoder models. We posit that any…
SemiReward: A General Reward Model for Semi-supervised Learning
Siyuan Li, Weiyang Jin, Zedong Wang +4
Semi-supervised learning (SSL) has witnessed great progress with various improvements in the self-training framework with pseudo labeling. The main challenge is how to distinguish…
A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource
Yue Liu, Jun Xia, Sihang Zhou +7
Graph clustering, which aims to divide nodes in the graph into several distinct clusters, is a fundamental yet challenging task. Benefiting from the powerful representation capabil…
SimVPv2: Towards Simple yet Powerful Spatiotemporal Predictive Learning
Cheng Tan, Zhangyang Gao, Siyuan Li +1
Recent years have witnessed remarkable advances in spatiotemporal predictive learning, with methods incorporating auxiliary inputs, complex neural architectures, and sophisticated…