Uncovering Hidden Connections and Functional Modules via pyPARAGON: a Hybrid Approach for Network Contextualization

Date

2024-1-22

Author

ARICI, Muslum Kaan

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State-of-the-art omics technologies provide molecular insights into various biological contexts, such as disease states, patients, and drug perturbations. Network inference and reconstruction methods utilize several omics datasets to create context-based networks that reveal the interactions of biomolecules and the functioning of cells. We compared the coverage of reference networks in several categories of prior knowledge, such as pathways, three-dimensional structures of interactions, and publication counts of genes/proteins to detect constraints in reference networks. Additionally, we examined the limitations of reconstruction algorithms by inferring signaling pathways. Contextualized network inference has several challenging issues: i) Hits from omics datasets are sparse in reference networks. ii) Interpretation methods can miss hidden knowledge that connects significant hits in omics datasets while evaluating multi-omics datasets. iii) Well-studied proteins in reference networks come along with bias in contextualization. iv) Highly connected nodes, or hubs, cause unspecific and noisy interactions in inferred networks. To overcome these challenges, we developed pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omics data integratioN). Combining network propagation with graphlets, pyPARAGON also improves precision and reduces the presence of non-specific interactions in contextualized networks. We tested the performance of pyPARAGON by reconstructing cancer-associated signaling pathways and setting contextual models of different cancer types. Moreover, pyPARAGON has promising performance in case studies such as tumor-specific networks with significant biological processes and contextualized neurodevelopmental disorders and cancer models, including signal strength on their shared pathways.

Subject Keywords

Network reconstruction, Graphlets, Data integration, Interactome, Disease modeling

URI

https://hdl.handle.net/11511/108481

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Graduate School of Informatics, Thesis