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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Published on: October 13, 2023

Protocol to implement a computational pipeline for biomedical discovery based on a biomedical knowledge graph.

Chang Su¹, Yu Hou², Michael Levin³

¹Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA.

|October 26, 2023

Summary

This summary is machine-generated.

This study introduces a computational pipeline for biomedical knowledge discovery (BKD) using biomedical knowledge graphs (BKGs). The pipeline aids in generating drug repurposing hypotheses for diseases like Parkinson's disease.

Keywords:

Bioinformatics Computer Sciences Health Sciences

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Area of Science:

Biomedical Informatics
Artificial Intelligence
Computational Biology

Background:

Biomedical knowledge graphs (BKGs) offer efficient management of vast biomedical data.
Artificial intelligence (AI) techniques facilitate knowledge mining from BKGs.
Discovering novel biomedical insights requires advanced computational approaches.

Purpose of the Study:

To present a protocol for a computational pipeline for biomedical knowledge discovery (BKD).
To demonstrate the application of this pipeline for generating drug repurposing hypotheses.
To facilitate efficient knowledge extraction from complex biomedical datasets.

Main Methods:

Developing a computational pipeline for BKD.
Utilizing knowledge graph embeddings for BKD.
Implementing data processing and prediction result interpretation.
Applying the pipeline to Parkinson's disease for drug repurposing.

Main Results:

The protocol enables the implementation of a BKD pipeline.
The pipeline successfully generates drug repurposing hypotheses.
Demonstrated utility in identifying potential therapeutic strategies for Parkinson's disease.

Conclusions:

The presented protocol offers a robust framework for BKD.
This approach enhances the discovery of novel biomedical knowledge.
The pipeline is valuable for accelerating drug repurposing research.