Papers with Code 아카이브

Protein Function Prediction

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하위 태스크: 1
평가표: 3
보존된 평가 기록: 15

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).

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/AzusaXuan/AnnoDPO

공식 코드

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization

https://github.com/EGiunchiglia/C-HMCNN

공식 코드

Coherent Hierarchical Multi-Label Classification Networks

https://github.com/WangZhiwei9/MIPE

공식 코드

Improving Paratope and Epitope Prediction by Multi-Modal Contrastive Learning and Interaction Informativeness Estimation

https://github.com/agemagician/Ankh

공식 코드

Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling

https://github.com/ai-hpc-research-team/protfad

공식 코드

ProtFAD: Introducing function-aware domains as implicit modality towards protein function prediction

https://github.com/blingbell/msngo

공식 코드

MSNGO: multi-species protein function annotation based on 3D protein structure and network propagation

https://github.com/borgwardtlab/pst

공식 코드

Endowing Protein Language Models with Structural Knowledge

https://github.com/boun-tabi-lifelu/evolutionary-subword-tokenization

공식 코드

evoBPE: Evolutionary Protein Sequence Tokenization

https://github.com/btbpanda/cafa5-protein-function-prediction-2nd-place

공식 코드

ProtBoost: protein function prediction with Py-Boost and Graph Neural Networks -- CAFA5 top2 solution

https://github.com/chiang-yuan/prodar

공식 코드

Encoding protein dynamic information in graph representation for functional residue identification

https://github.com/deepgraphlearning/esm-gearnet

공식 코드

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

https://github.com/deepgraphlearning/esm-s

공식 코드

Structure-Informed Protein Language Model

https://github.com/deepgraphlearning/gearnet

공식 코드

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

https://github.com/deepgraphlearning/peer_benchmark

공식 코드

PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding

https://github.com/dongkikim95/d-sla

공식 코드

Graph Self-supervised Learning with Accurate Discrepancy Learning

https://github.com/grizzledtroubadour/ProtFAD

공식 코드

ProtFAD: Introducing function-aware domains as implicit modality towards protein function prediction

https://github.com/hadi-abdine/Prot2Text

공식 코드

Prot2Text: Multimodal Protein's Function Generation with GNNs and Transformers

https://github.com/hirl-team/eurnet-image

공식 코드

EurNet: Efficient Multi-Range Relational Modeling of Spatial Multi-Relational Data

https://github.com/jiaqingxie/deepprotein

공식 코드

DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning

https://github.com/jlparkI/benchmarking_xGPR

공식 코드

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

https://github.com/jlparki/xgpr

공식 코드

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

https://github.com/liseda-lab/truewalks

공식 코드

Biomedical Knowledge Graph Embeddings with Negative Statements

https://github.com/markuswenzel/xai-proteins

공식 코드

Insights Into the Inner Workings of Transformer Models for Protein Function Prediction

https://github.com/mrzzmrzz/scop

공식 코드

SCOP: A Sequence-Structure Contrast-Aware Framework for Protein Function Prediction

https://github.com/paperswithcode/galai

공식 코드

Galactica: A Large Language Model for Science

https://github.com/samiscoding/stCFExt

공식 코드

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

https://github.com/snap-stanford/pretrain-gnns/

공식 코드

Strategies for Pre-training Graph Neural Networks

https://github.com/usccolumbia/md-hit

공식 코드

MD-HIT: Machine learning for materials property prediction with dataset redundancy control

https://github.com/xiangyue9607/BioNEV

공식 코드

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

https://github.com/zjunlp/mol-instructions

공식 코드

Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models

https://github.com/zjunlp/ontoprotein

공식 코드

OntoProtein: Protein Pretraining With Gene Ontology Embedding

https://github.com/18552433513/idontlikecode

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

https://github.com/LBM-EPFL/PeSTo

https://github.com/QustKcz/BioNEV

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

https://github.com/Wenlin-Chen/ADKF-IFT

Strategies for Pre-training Graph Neural Networks

https://github.com/aggelos-michael-papadopoulos/ParaSurf

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.