REDDA: integrating multiple biological relations to heterogeneous graph neural network for drug-disease association prediction
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Updated
Nov 11, 2024 - Python
REDDA: integrating multiple biological relations to heterogeneous graph neural network for drug-disease association prediction
MilGNet: Deep Multiple Instance Learning on Heterogeneous Graph for Drug-disease Association Prediction
Code for the paper "Clinical connectivity map for drug repurposing: using laboratory results to bridge drugs and diseases". Accepted by BMC Medical Informatics and Decision Making, 2021
Heter-LP is a novel semi-supervised heterogeneous label propagation algorithm.
Drug repositioning and synthetic patient DSS for pediatric ALL · TÜBİTAK 1001 · 123E383
drug repositioning method evaluation
使用 ML / DL 模型進行藥物與疾病之間的關聯預測
A Java backend for the DREIMT application
Toward a self-learning AI agent for drug repurposing: building human-scale representations for virtual patients. Module-panel evaluation benchmark: 332 modules, 1,916 drugs. Companion to the SteeraMed Bench paper.
Reproducible pipeline for interpretable drug repositioning using link prediction in drug–disease networks, combining network topology and chemical similarity.
Github Pages template for academic personal websites
subnetDR is an R/Python workflow for subtype-specific network module identification and drug repositioning.
An Angular 9 frontend for the DREIMT application
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