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20212026
most citedCo-learning: Learning from Noisy Labels with Self-supervision

122 citations · 196 across the 15 of their papers we have counts for

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18 papers · 1 filter

cs.LG20251 cited

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…

cs.LG20251 cited

Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design

Lang Yu, Zhangyang Gao, Cheng Tan +3

SE(3)-based generative models have shown great promise in protein geometry modeling and effective structure design. However, the field currently lacks a modularized benchmark to en…

cs.LG2025

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…

cs.LG2024

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…

cs.LG2024

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…

cs.LG2024

Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting

Lirong Wu, Haitao Lin, Guojiang Zhao +2

Recent years have witnessed great success in handling graph-related tasks with Graph Neural Networks (GNNs). However, most existing GNNs are based on message passing to perform fea…