The possibility to computationally prioritize candi- date disease genes capitalizing on existing information has led to a speedup in the discovery of new methods. Many gene discovery techniques exploit network data, like protein-protein interactions (PPIs), in order to extract knowledge from the network structure relying on several network metrics. We here present PROCONSUL, a method that builds on top of the concept of connectivity significance (CS) and exploits the idea of probabilistic exploration of the space of putative disease genes. We show that our methodology is able to outperform the state-of- the-art tool based on CS in several settings, and propose different, effective gene discovery strategies according to specific disease network properties.

PROCONSUL: PRObabilistic exploration of CONnectivity Significance patterns for disease modULe discovery

Paolo Tieri
2022

Abstract

The possibility to computationally prioritize candi- date disease genes capitalizing on existing information has led to a speedup in the discovery of new methods. Many gene discovery techniques exploit network data, like protein-protein interactions (PPIs), in order to extract knowledge from the network structure relying on several network metrics. We here present PROCONSUL, a method that builds on top of the concept of connectivity significance (CS) and exploits the idea of probabilistic exploration of the space of putative disease genes. We show that our methodology is able to outperform the state-of- the-art tool based on CS in several settings, and propose different, effective gene discovery strategies according to specific disease network properties.
2022
Istituto Applicazioni del Calcolo ''Mauro Picone''
bioinformatics
disease gene discovery
gene dis- ease association
interactome
network analysis
network medicine
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/414146
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