Protein Function Prediction
23 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
Latest papers with no code
HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning
In this paper, we propose a neural network model to address multiple tasks jointly upon the input of 3D protein structures.
Advances of Deep Learning in Protein Science: A Comprehensive Survey
Protein representation learning plays a crucial role in understanding the structure and function of proteins, which are essential biomolecules involved in various biological processes.
Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction
To study this problem, we identify a Protein 3D Graph Structure Learning Problem for Robust Protein Property Prediction (PGSL-RP3), collect benchmark datasets, and present a protein Structure embedding Alignment Optimization framework (SAO) to mitigate the problem of structure embedding bias between the predicted and experimental protein structures.
InstructProtein: Aligning Human and Protein Language via Knowledge Instruction
To address this challenge, we propose InstructProtein, an innovative LLM that possesses bidirectional generation capabilities in both human and protein languages: (i) taking a protein sequence as input to predict its textual function description and (ii) using natural language to prompt protein sequence generation.
Contrastive Learning for Non-Local Graphs with Multi-Resolution Structural Views
The contrastive methods are popular choices for learning the representation of nodes in a graph.
DeepGATGO: A Hierarchical Pretraining-Based Graph-Attention Model for Automatic Protein Function Prediction
Then, we use GATs to dynamically extract the structural information of non-Euclidean data, and learn general features of the label dataset with contrastive learning by constructing positive and negative example samples.
Self-supervised Learning and Graph Classification under Heterophily
Self-supervised learning has shown its promising capability in graph representation learning in recent work.
Reprogramming Pretrained Language Models for Protein Sequence Representation Learning
To this end, we reprogram an off-the-shelf pre-trained English language transformer and benchmark it on a set of protein physicochemical prediction tasks (secondary structure, stability, homology, stability) as well as on a biomedically relevant set of protein function prediction tasks (antimicrobial, toxicity, antibody affinity).
A Review of Deep Learning Techniques for Protein Function Prediction
Deep Learning and big data have shown tremendous success in bioinformatics and computational biology in recent years; artificial intelligence methods have also significantly contributed in the task of protein function classification.
Contrastive Representation Learning for 3D Protein Structures
Learning from 3D protein structures has gained wide interest in protein modeling and structural bioinformatics.
