Papers Protein Function Prediction
“Protein Function Prediction” 태그가 달린 논문 89편 · 필터 해제
Multi-modal Representation Learning Enables Accurate Protein Function Prediction in Low-Data Setting
In this study, we propose HOPER (HOlistic ProtEin Representation), a novel multimodal learning framework designed to enhance protein function prediction (PFP) in low-data settings. The challenge of predicting protein fun…
Protein Function PredictionRepresentation LearningTransfer LearningSCOP: A Sequence-Structure Contrast-Aware Framework for Protein Function Prediction
Improving the ability to predict protein function can potentially facilitate research in the fields of drug discovery and precision medicine. Technically, the properties of proteins are directly or indirectly reflected i…
Drug DiscoveryProtein Function PredictionHashing for Protein Structure Similarity Search
Protein structure similarity search (PSSS), which tries to search proteins with similar structures, plays a crucial role across diverse domains from drug design to protein function prediction and molecular evolution. Tra…
Drug DesignProtein Function PredictionDeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning
Deep learning has deeply influenced protein science, enabling breakthroughs in predicting protein properties, higher-order structures, and molecular interactions. This paper introduces DeepProtein, a comprehensive and us…
Deep LearningDrug DiscoveryGraph Neural NetworkPrediction+2Protein-Mamba: Biological Mamba Models for Protein Function Prediction
Protein function prediction is a pivotal task in drug discovery, significantly impacting the development of effective and safe therapeutics. Traditional machine learning models often struggle with the complexity and vari…
Drug DiscoveryMambaPredictionProtein Function Prediction+1ProteinRPN: Towards Accurate Protein Function Prediction with Graph-Based Region Proposals
Protein function prediction is a crucial task in bioinformatics, with significant implications for understanding biological processes and disease mechanisms. While the relationship between sequence and function has been …
Protein Function PredictionRegion ProposalAutoregressive Enzyme Function Prediction with Multi-scale Multi-modality Fusion
Accurate prediction of enzyme function is crucial for elucidating biological mechanisms and driving innovation across various sectors. Existing deep learning methods tend to rely solely on either sequence data or structu…
PredictionProtein Function PredictionA Vectorization Method Induced By Maximal Margin Classification For Persistent Diagrams
Persistent homology is an effective method for extracting topological information, represented as persistent diagrams, of spatial structure data. Hence it is well-suited for the study of protein structures. Attempts to i…
Binary ClassificationProtein Function PredictionTopological Data AnalysisGeometric Self-Supervised Pretraining on 3D Protein Structures using Subgraphs
Protein representation learning aims to learn informative protein embeddings capable of addressing crucial biological questions, such as protein function prediction. Although sequence-based transformer models have shown …
Protein Function PredictionRepresentation LearningProtFAD: Introducing function-aware domains as implicit modality towards protein function prediction
Protein function prediction is currently achieved by encoding its sequence or structure, where the sequence-to-function transcendence and high-quality structural data scarcity lead to obvious performance bottlenecks. Pro…
Contrastive LearningProtein Function PredictionTripletProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering
Large language models (LLMs) have garnered considerable attention for their proficiency in tackling intricate tasks, particularly leveraging their capacities for zero-shot and in-context learning. However, their utility …
In-Context LearningProtein DesignProtein Function PredictionHeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning
Understanding and leveraging the 3D structures of proteins is central to a variety of biological and drug discovery tasks. While deep learning has been applied successfully for structure-based protein function prediction…
Drug DiscoveryGraph Neural NetworkMulti-Task LearningProperty Prediction+1Advances 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. In recent years, deep learning has …
Deep LearningDrug DiscoveryProtein Function PredictionProtein Structure Prediction+2Structure-Informed Protein Language Model
Protein language models are a powerful tool for learning protein representations through pre-training on vast protein sequence datasets. However, traditional protein language models lack explicit structural supervision, …
Language ModelingLanguage ModellingmodelPrediction+2Endowing Protein Language Models with Structural Knowledge
Understanding the relationships between protein sequence, structure and function is a long-standing biological challenge with manifold implications from drug design to our understanding of evolution. Recently, protein la…
Drug DesignLanguage ModelingLanguage ModellingMasked Language Modeling+2Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction
Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimation. The existing methods highly rely on …
Graph structure learningPredictionProperty PredictionProtein Function Prediction+1InstructProtein: Aligning Human and Protein Language via Knowledge Instruction
Large Language Models (LLMs) have revolutionized the field of natural language processing, but they fall short in comprehending biological sequences such as proteins. To address this challenge, we propose InstructProtein…
Knowledge GraphsProtein Function PredictionText GenerationInsights Into the Inner Workings of Transformer Models for Protein Function Prediction
Motivation: We explored how explainable artificial intelligence (XAI) can help to shed light into the inner workings of neural networks for protein function prediction, by extending the widely used XAI method of integrat…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Protein Function PredictionContrastive Learning for Non-Local Graphs with Multi-Resolution Structural Views
Learning node-level representations of heterophilic graphs is crucial for various applications, including fraudster detection and protein function prediction. In such graphs, nodes share structural similarity identified …
Contrastive LearningProtein Function PredictionBiomedical Knowledge Graph Embeddings with Negative Statements
A knowledge graph is a powerful representation of real-world entities and their relations. The vast majority of these relations are defined as positive statements, but the importance of negative statements is increasingl…
Graph EmbeddingGraph Representation LearningKnowledge Graph EmbeddingKnowledge Graph Embeddings+5