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Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery

Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discov...

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Autores principales: Domingo-Fernández, Daniel, Gadiya, Yojana, Patel, Abhishek, Mubeen, Sarah, Rivas-Barragan, Daniel, Diana, Chris W., Misra, Biswapriya B., Healey, David, Rokicki, Joe, Colluru, Viswa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8906585/
https://www.ncbi.nlm.nih.gov/pubmed/35213534
http://dx.doi.org/10.1371/journal.pcbi.1009909
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author Domingo-Fernández, Daniel
Gadiya, Yojana
Patel, Abhishek
Mubeen, Sarah
Rivas-Barragan, Daniel
Diana, Chris W.
Misra, Biswapriya B.
Healey, David
Rokicki, Joe
Colluru, Viswa
author_facet Domingo-Fernández, Daniel
Gadiya, Yojana
Patel, Abhishek
Mubeen, Sarah
Rivas-Barragan, Daniel
Diana, Chris W.
Misra, Biswapriya B.
Healey, David
Rokicki, Joe
Colluru, Viswa
author_sort Domingo-Fernández, Daniel
collection PubMed
description Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two case studies showing how one can deconvolute the predictions made by RPath as well as predict novel targets.
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spelling pubmed-89065852022-03-10 Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery Domingo-Fernández, Daniel Gadiya, Yojana Patel, Abhishek Mubeen, Sarah Rivas-Barragan, Daniel Diana, Chris W. Misra, Biswapriya B. Healey, David Rokicki, Joe Colluru, Viswa PLoS Comput Biol Research Article Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two case studies showing how one can deconvolute the predictions made by RPath as well as predict novel targets. Public Library of Science 2022-02-25 /pmc/articles/PMC8906585/ /pubmed/35213534 http://dx.doi.org/10.1371/journal.pcbi.1009909 Text en © 2022 Domingo-Fernández et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Domingo-Fernández, Daniel
Gadiya, Yojana
Patel, Abhishek
Mubeen, Sarah
Rivas-Barragan, Daniel
Diana, Chris W.
Misra, Biswapriya B.
Healey, David
Rokicki, Joe
Colluru, Viswa
Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery
title Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery
title_full Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery
title_fullStr Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery
title_full_unstemmed Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery
title_short Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery
title_sort causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8906585/
https://www.ncbi.nlm.nih.gov/pubmed/35213534
http://dx.doi.org/10.1371/journal.pcbi.1009909
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