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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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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. |
format | Online Article Text |
id | pubmed-8906585 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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