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

1.5K Views

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07:35 min

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October 13th, 2023

DOI :

10.3791/65084-v

October 13th, 2023

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Alexander R. Pelletier¹^,²^,³, Dylan Steinecke¹^,³^,⁴, Dibakar Sigdel¹, Irsyad Adam¹, J. Harry Caufield¹, Vladimir Guevara-Gonzalez¹, Joseph Ramirez¹, Aarushi Verma¹, Kaitlyn Bali¹, Katherine Downs¹, Wei Wang¹^,²^,³, Alex Bui³^,⁴, Peipei Ping¹^,²^,³^,⁴^,⁵

¹Department of Physiology, UCLA School of Medicine, ²Scalable Analytics Institute (ScAi) at Department of Computer Science, UCLA School of Engineering, ³NIH BRIDGE2AI Center at UCLA & NHLBI Integrated Cardiovascular Data Science Training Program, UCLA, ⁴Medical Informatics, University of California at Los Angeles (UCLA), ⁵Department of Medicine (Cardiology), UCLA School of Medicine

Summary

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Computational Protocol

Protein Disease Associations

Hypothesis Generation

Biomedical Publications

Therapeutic Insights

Chapters in this video

0:04

Introduction

1:09

Running the Docker Container

1:53

Preparing the Diseases and Proteins

3:16

Analyzing the Results

4:20

Predictive Analysis

5:40

Results: Top Protein‐Disease and Pathway‐Disease Associations and Application of Deep Learning for Knowledge Graph Completion

6:40

Conclusion

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