Augmenting Signaling Pathway Reconstructions

Rubel, Tobias; Ritz, Anna

doi:10.1145/3388440.3412411

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…In addition to GGN construction, the personalized PageRank algorithm propagates signals from seed nodes across the reference interactome. Node weights after propagation together with their degrees and edge confidence scores are combined in a single function to calculate edge fluxes (see Methods) 44 . In this function, the degree component penalizes highly connected proteins that are non-specifically present in the resulting subnetworks.…”

Section: Overview Of Pyparagon As a Hybrid Network Inference Frameworkmentioning

confidence: 99%

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Unveiling Hidden Connections in Omics DataviapyPARAGON: an Integrative Hybrid Approach for Disease Network Construction

Arici,

Tuncbag

2023

Preprint

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Omics technologies are powerful tools for detecting dysregulated and altered signaling components in various contexts, encompassing disease states, patients, and drug-perturbations. Network inference or reconstruction algorithms play an integral role in the successful analysis and identification of causal relationships between omics hits. 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. By leveraging network motifs instead of pairwise connections among proteins, pyPARAGON offers improved accuracy and reduces the inclusion of nonspecific interactions in signaling networks. Through comprehensive evaluations on benchmark cancer 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, leading to the discovery of tumor-specific signaling pathways. Overall, the development and evaluation of pyPARAGON significantly contributes to the field as an effective tool for the analysis and integration of multi-omic data in the context of signaling networks. pyPARAGON is available at https://github.com/metunetlab/pyPARAGON.

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Section: Overview Of Pyparagon As a Hybrid Network Inference Frameworkmentioning

confidence: 99%

“…We calculated the directional flux scores for both directions (f u→t and f t→u ) by using formulas 2 and 3, where u, t ∈ V R and e is the edge between u and t, and deg(u), deg(t) are the number of neighbors of nodes, u and t, respectively 44 . The negative logarithm of minimum flux scores is used as a final edge (f(e)) score defined in formula 4.…”

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confidence: 99%

Unveiling Hidden Connections in Omics DataviapyPARAGON: an Integrative Hybrid Approach for Disease Network Construction

Arici,

Tuncbag

2023

Preprint

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Neurodevelopmental disorders and cancer networks share pathways, but differ in mechanisms, signaling strength, and outcome

Yavuz,

Arici,

Demirel

et al. 2023

npj Genom. Med.

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Epidemiological studies suggest that individuals with neurodevelopmental disorders (NDDs) are more prone to develop certain types of cancer. Notably, however, the case statistics can be impacted by late discovery of cancer in individuals afflicted with NDDs, such as intellectual disorders, autism, and schizophrenia, which may bias the numbers. As to NDD-associated mutations, in most cases, they are germline while cancer mutations are sporadic, emerging during life. However, somatic mosaicism can spur NDDs, and cancer-related mutations can be germline. NDDs and cancer share proteins, pathways, and mutations. Here we ask (i) exactly which features they share, and (ii) how, despite their commonalities, they differ in clinical outcomes. To tackle these questions, we employed a statistical framework followed by network analysis. Our thorough exploration of the mutations, reconstructed disease-specific networks, pathways, and transcriptome levels and profiles of autism spectrum disorder (ASD) and cancers, point to signaling strength as the key factor: strong signaling promotes cell proliferation in cancer, and weaker (moderate) signaling impacts differentiation in ASD. Thus, we suggest that signaling strength, not activating mutations, can decide clinical outcome.

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Growing Directed Acyclic Graphs: Optimization Functions for Pathway Reconstruction Algorithms

Ritz

2023

Journal of Computational Biology

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Augmenting Signaling Pathway Reconstructions

Cited by 6 publications

References 33 publications

Unveiling Hidden Connections in Omics DataviapyPARAGON: an Integrative Hybrid Approach for Disease Network Construction

Unveiling Hidden Connections in Omics DataviapyPARAGON: an Integrative Hybrid Approach for Disease Network Construction

Neurodevelopmental disorders and cancer networks share pathways, but differ in mechanisms, signaling strength, and outcome

Growing Directed Acyclic Graphs: Optimization Functions for Pathway Reconstruction Algorithms

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