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biomedgps

imported

therapeutics/biomedgps

A knowledge graph system with graph neural network for drug repurposing and disease mechanism.

Machine-generated from the listed sources and not yet reviewed by a human.

biomedgps project image
GitHub preview card for open-prophetdb/biomedgps. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Therapeutics
Subcategory
unknown
License
AGPL-3.0(osi)
Status
maintained
Maturity
deployed
Organization
open-prophetdb
Country
unknown
Documentation
unknown
Tags
disease-mechanisms · disease-prediction · drug-discovery · drug-repurposing · graph-embedding · graph-neural-network · knowledge-graph
Regulatory
unknown
similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

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  • 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

  • PiLSLdrug-discovery · graph-neural-network

    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…

  • Pocket2Drugdrug-discovery · graph-neural-network

    Pytorch implementation of Pocket2Drug: a generative deep learning model to predict binding drugs for ligand-binding sites.

  • biomedical-kg-topological-imbalancedrug-discovery · knowledge-graph

    Code to accompany the "Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs" (Briefings in Bioinformatics, 2022)

sources
  1. api.github.com/repos/open-prophetdb/biomedgps
    retrieved 2026-08-25 · via github-api

    Machine-imported from GitHub search. Last push 2025-09-12, 18 stars, license reported as AGPL-3.0. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/3.json→ .entries["biomedgps"]

Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.