PiLSL
importedtherapeutics/pilsl
PiLSL is a pairwise interaction learning-based graph neural network (GNN) model for prediction of synthetic lethality (SL) as anti-cancer drug targets. It learns the representation of pairwise…
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Therapeutics
- Subcategory
- unknown
- License
- MIT(osi)
- Status
- dormant
- Maturity
- deployed
- Organization
- unknown
- Country
- unknown
- Homepage
- unknown
- Repository
- github.com/JieZheng-ShanghaiTech/PiLSL
- Documentation
- unknown
- Tags
- attention-mechanism · bioinformatics · data-science · drug-discovery · graph-neural-network · machine-learning
- Regulatory
- unknown
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- Pocket2Drugbioinformatics · drug-discovery · graph-neural-network
Pytorch implementation of Pocket2Drug: a generative deep learning model to predict binding drugs for ligand-binding sites.
- biomedgpsdrug-discovery · graph-neural-network
A knowledge graph system with graph neural network for drug repurposing and disease mechanism.
- HAG-Netdrug-discovery · graph-neural-network
Code for "Enhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations"
- kaggle_leash_belkadrug-discovery · graph-neural-network
11th place solution of NeurIPS 2024 - Predict New Medicines with BELKA competition on Kaggle: https://www.kaggle.com/competitions/leash-BELKA
- Graphsitebioinformatics · graph-neural-network
Generate graph representations of protein binding sites.
- DeepAffinityattention-mechanism · drug-discovery
Protein-compound affinity prediction through unified RNN-CNN
- api.github.com/repos/JieZheng-ShanghaiTech/PiLSLretrieved 2026-08-25 · via github-api
Machine-imported from GitHub search. Last push 2024-12-04, 13 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/5.json→ .entries["pilsl"]
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