Protein Function Prediction
24 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).
Benchmarks
These leaderboards are used to track progress in Protein Function Prediction
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FLIP
Most implemented papers
Strategies for Pre-training Graph Neural Networks
snap-stanford/pretrain-gnns
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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
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
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
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
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
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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
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
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
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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
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.
