RANKPCSF: A DISEASE MODULE IDENTIFICATION METHOD BY INTEGRATING NETWORK PROPAGATION WITH PRIZE-COLLECTING STEINER FOREST

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2022-9-2

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Eskin, Arda

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Identification of disease modules may lead to a better understanding on the progression of diseases, finding more accurate biomarkers, and drug targets. In this study, we develop a hybrid method – RANKPCSF- combining the strength of Steiner tree and diffusion approaches and release a new network reconstruction approach. RANKPCSF is capable to integrate multi-omic data (including phosphoproteomic and transcriptomic) with a reference interactome to unveil the optimal disease-associated network. We have compared RANKPCSF's performance on predicting known cancer genes and drug targets with NetCore, Hierarchical HotNet and DOMINO. On average, RANKPCSF was able to capture cancer driver genes and cancer drug targets with higher precision and identified more cancer genes from the set of genes that were deemed not significant in the pan-cancer experiment. Next, we compared the functional relevancy of the resulting networks from RANKPCSF and other methods. NetCore and RANKPCSF gave the highest functional relevances based on empirical p-values (0.001). Finally, we have applied RANKPCSF to reconstruct ischemic heart disease (IHD)-associated network by using the transcriptomic data from 46 patients as an independent validation case study. We observed modules related to oxidative stress, extracellular matrix organization, and immune response, which were relevant to ischemic heart disease pathology. Overall, RANKPCSF captures known cancer genes and drug targets more accurately and performed better on functional relevance test compared to other selected algorithms. We believe that RANKPCSF – as a hybrid method - can be used for different tasks including functionally relevant subnetworks, and these subnetworks can be used to generate disease-associated hypotheses.

Subject Keywords

Bioinformatics, Systems Biology, Network Medicine, Disease Module

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https://hdl.handle.net/11511/98804

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Graduate School of Informatics, Thesis

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Citation Formats

A. Eskin, “RANKPCSF: A DISEASE MODULE IDENTIFICATION METHOD BY INTEGRATING NETWORK PROPAGATION WITH PRIZE-COLLECTING STEINER FOREST,” M.S. - Master of Science, Middle East Technical University, 2022.