The possibility to computationally prioritize candidate 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, 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Pages 1941-1947
PROCONSUL: PRObabilistic exploration of CONnectivity Significance patterns for disease modULe discovery (04b Atto di convegno in volume)
Luca Riccardo De, Carfora Marco, Blanco Gonzalo, Mastropietro Andrea, Petti Manuela, Tieri Paolo
Gruppo di ricerca: Algorithms and Data Science