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Unveiling hidden connections in omics data via pyPARAGON: an integrative hybrid approach for disease network construction
Date
2024-09-01
Author
Arici, Muslum Kaan
Tunçbağ, Nurcan
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Network inference or reconstruction algorithms play an integral role in successfully analyzing and identifying causal relationships between omics hits for detecting dysregulated and altered signaling components in various contexts, encompassing disease states and drug perturbations. However, accurate representation of signaling networks and identification of context-specific interactions within sparse omics datasets in complex interactomes pose significant challenges in integrative approaches. To address these challenges, we present pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omic data integratioN), a novel tool that combines network propagation with graphlets. pyPARAGON enhances accuracy and minimizes the inclusion of nonspecific interactions in signaling networks by utilizing network rather than relying on pairwise connections among proteins. Through comprehensive evaluations on benchmark signaling pathways, we demonstrate that pyPARAGON outperforms state-of-the-art approaches in node propagation and edge inference. Furthermore, pyPARAGON exhibits promising performance in discovering cancer driver networks. Notably, we demonstrate its utility in network-based stratification of patient tumors by integrating phosphoproteomic data from 105 breast cancer tumors with the interactome and demonstrating tumor-specific signaling pathways. Overall, pyPARAGON is a novel tool for analyzing and integrating multi-omic data in the context of signaling networks. pyPARAGON is available at https://github.com/netlabku/pyPARAGON.
Subject Keywords
data integration
,
graphlets
,
interactome
,
network reconstruction
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85201778670&origin=inward
https://hdl.handle.net/11511/111111
Journal
Briefings in Bioinformatics
DOI
https://doi.org/10.1093/bib/bbae399
Collections
Graduate School of Informatics, Article
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BibTeX
M. K. Arici and N. Tunçbağ, “Unveiling hidden connections in omics data via pyPARAGON: an integrative hybrid approach for disease network construction,”
Briefings in Bioinformatics
, vol. 25, no. 5, pp. 0–0, 2024, Accessed: 00, 2024. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85201778670&origin=inward.