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medical-5

Protein Function Prediction

3260 papers • 126 benchmarks • 313 datasets

For GO terms prediction, given the specific function prediction instruction and a protein sequence, models characterize the protein functions using the GO terms presented in three different domains (cellular component, biological process, and molecular function).

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Benchmarks

These leaderboards are used to track progress in protein-function-prediction-5

Trend

Dataset

Best Model

Actions

CASPSimSeq

CASPSimSeq

PaenSeq

PaenSeq

UniProtSeq

UniProtSeq

Libraries

i

Use these libraries to find protein-function-prediction-5 models and implementations

Datasets

FLIP

FLIP -- AAV, Designed vs mutant

FLIP -- AAV, Designed vs mutant

Subtasks

Antibody-antigen binding prediction

Most implemented papers

Strategies for Pre-training Graph Neural Networks

Weihua Hu, M. Zitnik, Bowen Liu, J. Leskovec, Percy Liang, Joseph Gomes, V. Pande•Tue May 28 2019

A new strategy and self-supervised methods for pre-training Graph Neural Networks (GNNs) that avoids negative transfer and improves generalization significantly across downstream tasks, leading up to 9.4% absolute improvements in ROC-AUC over non-pre-trained models and achieving state-of-the-art performance for molecular property prediction and protein function prediction.

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

1678

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A Systematic Study of Joint Representation Learning on Protein Sequences and Structures

Vijil Chenthamarakshan, Payel Das, Zuobai Zhang, Chuanrui Wang, Minghao Xu, A. Lozano, Jian Tang•Fri Mar 10 2023

This study undertakes a comprehensive exploration of joint protein representation learning by integrating a state-of-the-art PLM (ESM-2) with distinct structure encoders (GVP, GearNet, CDConv) and achieves significant improvements over existing sequence- and structure-based methods.

52 0

Linear-Scaling Kernels for Protein Sequences and Small Molecules Outperform Deep Learning While Providing Uncertainty Quantitation and Improved Interpretability

Wen Wang, J. Parkinson•Mon Feb 06 2023

It is found that GP regression achieves highly competitive accuracy for these tasks while providing with well-calibrated uncertainty quantitation and improved interpretability and xGPR may be used as part of an active learning strategy to engineer a protein with a desired property in an automated way without human intervention.

11 0

AFDP: An Automated Function Description Prediction Approach to Improve Accuracy of Protein Function Predictions

S. Jozashoori, Amir Jozashoori, H. Schoof•Mon Oct 14 2019

AFDP is devised, an integrated approach for protein function prediction which benefits from the combination of two available tools, AHRD and eggNOG, to predict the functionality of novel proteins and produce more precise human readable descriptions by applying the stCFExt algorithm.

1 0

Coherent Hierarchical Multi-Label Classification Networks

Eleonora Giunchiglia, Thomas Lukasiewicz•Mon Oct 19 2020

This paper proposes C-HMCNN(h), a novel approach for HMC problems, which, given a network h for the underlying multi-label classification problem, exploits the hierarchy information in order to produce predictions coherent with the constraint and improve performance.

120 0

Structure-Enhanced Meta-Learning For Few-Shot Graph Classification

Xiangnan He, Fuli Feng, Shunyu Jiang, Weijian Chen•Thu Mar 04 2021

This work explores the potential of metric-based meta-learning for solving few-shot graph classification and proposes a novel framework which explicitly considers global structure and local structure of the input graph.

20 0

Encoding protein dynamic information in graph representation for functional residue identification

Chiang Yuan, Weiwei Hui, Shu-Wei Chang•Tue Dec 14 2021

This model successfully learns the dynamic fingerprints of proteins and provides molecular insights into protein functions, with vast untapped potential for broad biotechnology and pharmaceutical applications.

14 0

OntoProtein: Protein Pretraining With Gene Ontology Embedding

Ningyu Zhang, Shumin Deng, Zhen Bi, Xiaozhuan Liang, Huajun Chen, Siyuan Cheng, Haosen Hong, J. Lian, Qiang Zhang•Sat Jan 22 2022

This work proposes OntoProtein, the first general framework that makes use of structure in GO (Gene Ontology) into protein pre-training models, and constructs a novel large-scale knowledge graph that consists of GO and its related proteins, and gene annotation texts or protein sequences describe all nodes in the graph.

121 0

Graph Self-supervised Learning with Accurate Discrepancy Learning

Sung Ju Hwang, Jinheon Baek, Dongki Kim•Sun Feb 06 2022

This work proposes a framework that aims to learn the exact discrepancy between the original and the perturbed graphs, coined as Discrepancy-based Self-supervised LeArning (D-SLA), and validate it on various graph-related downstream tasks, on which the authors' largely outperforms relevant baselines.

43 0

Adding a benchmark result helps the community track progress.

Protein Function Prediction | State-of-the-Art