Protein Function Prediction

26 papers with code • 3 benchmarks • 2 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).

Most implemented papers

Strategies for Pre-training Graph Neural Networks

snap-stanford/pretrain-gnns ICLR 2020

Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training.

Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations

xiangyue9607/BioNEV 12 Jun 2019

Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis.

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

deepgraphlearning/gearnet 11 Mar 2023

Recent sequence representation learning methods based on Protein Language Models (PLMs) excel in sequence-based tasks, but their direct adaptation to tasks involving protein structures remains a challenge.

Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretability

jlparki/xgpr 7 Feb 2023

We compare the performance of xGPR with the reported performance of various deep learning models on 20 benchmarks, including small molecule, protein sequence and tabular data.

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

samiscoding/stCFExt 15 Oct 2019

Here, we devise AFDP, 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 our stCFExt algorithm.

Coherent Hierarchical Multi-Label Classification Networks

EGiunchiglia/C-HMCNN NeurIPS 2020

Hierarchical multi-label classification (HMC) is a challenging classification task extending standard multi-label classification problems by imposing a hierarchy constraint on the classes.

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

jiangshunyu/SMF-GIN 5 Mar 2021

Graph classification is a highly impactful task that plays a crucial role in a myriad of real-world applications such as molecular property prediction and protein function prediction. Aiming to handle the new classes with limited labeled graphs, few-shot graph classification has become a bridge of existing graph classification solutions and practical usage. This work explores the potential of metric-based meta-learning for solving few-shot graph classification. We highlight the importance of considering structural characteristics in the solution and propose a novel framework which explicitly considers global structure and local structure of the input graph.

Encoding protein dynamic information in graph representation for functional residue identification

chiang-yuan/prodar 15 Dec 2021

Recent advances in protein function prediction exploit graph-based deep learning approaches to correlate the structural and topological features of proteins with their molecular functions.

OntoProtein: Protein Pretraining With Gene Ontology Embedding

zjunlp/ontoprotein ICLR 2022

We construct 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.

Graph Self-supervised Learning with Accurate Discrepancy Learning

dongkikim95/d-sla 7 Feb 2022

Contrastive learning, while it can learn global graph-level similarities, its objective to maximize the similarity between two differently perturbed graphs may result in representations that cannot discriminate two similar graphs with different properties.