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Protein Function Prediction
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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).
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Into the Unknown: From Structure to Disorder in Protein Function Prediction
STELLA: Towards Protein Function Prediction with Multimodal LLMs Integrating Sequence-Structure Representations
MSNGO: multi-species protein function annotation based on 3D protein structure and network propagation
ProtTeX: Structure-In-Context Reasoning and Editing of Proteins with Large Language Models
evoBPE: Evolutionary Protein Sequence Tokenization
Computational Protein Science in the Era of Large Language Models (LLMs)
GoBERT: Gene Ontology Graph Informed BERT for Universal Gene Function Prediction
ProtCLIP: Function-Informed Protein Multi-Modal Learning
ProtBoost: protein function prediction with Py-Boost and Graph Neural Networks -- CAFA5 top2 solution
Multi-modal Representation Learning Enables Accurate Protein Function Prediction in Low-Data Setting
SCOP: A Sequence-Structure Contrast-Aware Framework for Protein Function Prediction
Hashing for Protein Structure Similarity Search
DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning
Protein-Mamba: Biological Mamba Models for Protein Function Prediction
ProteinRPN: Towards Accurate Protein Function Prediction with Graph-Based Region Proposals
Autoregressive Enzyme Function Prediction with Multi-scale Multi-modality Fusion
A Vectorization Method Induced By Maximal Margin Classification For Persistent Diagrams
Geometric Self-Supervised Pretraining on 3D Protein Structures using Subgraphs
Improving Paratope and Epitope Prediction by Multi-Modal Contrastive Learning and Interaction Informativeness Estimation
ProtFAD: Introducing function-aware domains as implicit modality towards protein function prediction
ProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering
HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning
Advances of Deep Learning in Protein Science: A Comprehensive Survey
Structure-Informed Protein Language Model
Endowing Protein Language Models with Structural Knowledge
Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction
InstructProtein: Aligning Human and Protein Language via Knowledge Instruction
Insights Into the Inner Workings of Transformer Models for Protein Function Prediction
Contrastive Learning for Non-Local Graphs with Multi-Resolution Structural Views
Biomedical Knowledge Graph Embeddings with Negative Statements
Prot2Text: Multimodal Protein's Function Generation with GNNs and Transformers
DeepGATGO: A Hierarchical Pretraining-Based Graph-Attention Model for Automatic Protein Function Prediction
MD-HIT: Machine learning for materials property prediction with dataset redundancy control
Self-supervised Learning and Graph Classification under Heterophily
Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models
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