prakhargurawa/Drug-Similarity-and-Link-Prediction-using-Graph-Embeddings-on-Medical-Knowledge-Graph
Utilizing graphical neural networks and embeddings on a medical database KEGG to perform link predictions and drug similarity systems.
This project helps medical researchers and pharmaceutical scientists explore relationships between drugs, diseases, genes, and biological pathways. By analyzing existing medical knowledge graphs, it takes information about various biological entities and outputs predictions about new connections or how similar different drugs are. This tool is for professionals in drug discovery, pharmacology, and bioinformatics.
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Use this if you need to identify potential new drug targets, understand disease mechanisms, or find drugs with similar therapeutic effects based on complex biological network data.
Not ideal if you are looking for clinical trial management, patient-specific diagnostic tools, or direct therapeutic recommendations.
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17
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5
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Oct 02, 2021
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