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Protein Function Prediction

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전체 기법 전체 데이터셋

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/eliberis/parapred

https://github.com/fransou/is-meta-learning-necessary

Strategies for Pre-training Graph Neural Networks

https://github.com/gnn4dr/DRKG

Strategies for Pre-training Graph Neural Networks

https://github.com/hld67890/prml2020_pj

Strategies for Pre-training Graph Neural Networks

https://github.com/jacquesboitreaud/DeepFRED

Strategies for Pre-training Graph Neural Networks

https://github.com/jacquesboitreaud/rna_ne

Strategies for Pre-training Graph Neural Networks

https://github.com/jiangshunyu/SMF-GIN

Structure-Enhanced Meta-Learning For Few-Shot Graph Classification

https://github.com/jianqingzheng/XBCR-net

https://github.com/leffff/any-domain-pretrain-gnns

Strategies for Pre-training Graph Neural Networks

https://github.com/maplightrx/maplight-tdc

Strategies for Pre-training Graph Neural Networks

https://github.com/microsoft/fs-mol

Strategies for Pre-training Graph Neural Networks

https://github.com/nicwulab/HA_Abs

https://github.com/oxpig/Paragraph

https://github.com/sebastiandaberdaku/AntibodyInterfacePrediction

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

Strategies for Pre-training Graph Neural Networks

https://github.com/sunlab-osu/BioNEV

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

https://github.com/vamships/PECAN

https://github.com/vsomnath/holoprot

Multi-Scale Representation Learning on Proteins

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