Protein Function Prediction

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9 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Effective Resistance and Graph Neural Network Reliability in Tissue-Specific Interactomes
Oct 1, 2026Jianru ShenGraph Neural Networks Protein Function Prediction
University of Montana Missoula, MT, USA
Do Vision CIL Methods Transfer to Protein? A Diagnostic Benchmark of Frozen Protein Encoders
Oct 1, 2026Ziyuan GaoProtein Function Prediction Class-Incremental Learning
Department of Computer Science and Technology, University of Cambridge
QLoRA Fine-Tuning of Ministral LLM for Sequence-to-Function Protein Annotation
Sep 21, 2026Demian Pavlyshenko, Bohdan PavlyshenkoLLM Fine-Tuning LLM-Assisted Annotation
Hyper-Fold: Exploring the Expressive Limit of Sequence-Geometry Learning for Proteins via Hypergraph Modeling
Aug 29, 2026Yifan Feng, Guanjie Cheng, Shihui Ying +2Protein Function Prediction Hypergraph Neural Networks
1{School of Software, BNRist, THUIBCS}, Tsinghua University, Beijing 100084, China · School of Software Technology, Zhejiang University, Ningbo 315100, China · 3Shanghai Institute of Applied Mathematics and Mechanics, School of Mechanics and Engineering Science, Shanghai University, Shanghai 200072, China +1
Federation Is Nearly Free, Reasoning Is Not: Tradeoffs for AI Co-Scientists in Protein Characterization Workflows
Aug 25, 2026Maia Kapur, Timothy Boe, Abby Jerger +1Protein Function Prediction Scientific Workflow Automation
Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, 3335 Innovation Blvd, Richland, WA, USA 99354 · Research Computing and Continuum Platform Engineering, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, USA 99352
Knowledge before Reasoning: EC-Reason-Bench, a Training-Free Diagnostic Benchmark for LLM Enzyme Classification
Jul 29, 2026Linyu Li, Zhi Jin, Yichi Zhang +6Retrieval-Augmented Generation LLM Evaluation
School of Computer Science, Peking University, Beijing, China · College of Computer Science and Technology, Zhejiang University, Hangzhou, China · School of Computer Science, Tibet University, Lhasa, China
Learning Protein Structure-Function Relationships through Knowledge-guided Representation Decomposition
May 12, 2026Mingqing Wang, Zhiwei Nie, Athanasios V. Vasilakos +2Representation Disentanglement Protein Function Prediction
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · Pengcheng Laboratory, Shenzhen, China · School of Electronic and Computer Engineering, Peking University, Shenzhen, China +2
Structural Interpretations of Protein Language Model Representations via Differentiable Graph Partitioning
May 9, 2026Siddhant Dutta, Edward Tan Beng Wai, Soumick Sarker +2Graph Neural Networks Protein Language Models
Nanyang Technological University, Singapore
Better Protein Function Prediction by Modeling Survivorship Bias
May 7, 2026Zhongmou Chao, Poompol Buathong, Ekaterina Selivanovitch +2Protein Function Prediction Weakly Supervised Learning
Smith School of Chemical and Biomolecular Engineering, Cornell University, USA · Center for Applied Mathematics, Cornell University, USA · School of Operations Research and Information Engineering, Cornell University, USA

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Effective Resistance and Graph Neural Network Reliability in Tissue-Specific Interactomes

Jianru Shen

University of Montana Missoula, MT, USA

Graph Neural Networks Protein Function Prediction

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Protein function annotation needs to know which predictions to distrust, not only what a model predicts. We ask whether tissue-specific interaction structure carries that information. Our candidate signal is effective resistance, used previously to relieve over-squashing by rewiring. Across 24 tissue-specific interactomes it is dominated by inverse degree, and the degeneration deepens as the co-expression filtered network grows, with a Spearman correlation of -0.955. The residual departure from that limit exceeds degree-preserving null graphs in all 24 networks. Controlling for predictive entropy, degree, annotation cardinality, local structure and feature-only difficulty, the residual explains additional per-node loss in 19 of 24 held-out networks once a permutation floor is subtracted, at every depth, and the effect strengthens monotonically with depth. The increment reaches 0.37% of the variance the controls leave unexplained, 5.6 times a permutation floor, against 1.5 times when the model is retrained in a degree-preserving null world. Selective prediction improves negligibly. The signal is reproducible; degree degeneration bounds it.