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Article

MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction

Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy
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Author to whom correspondence should be addressed.
Academic Editors: Stefano Lonardi and Sven Rahmann
Genes 2021, 12(11), 1713; https://doi.org/10.3390/genes12111713
Received: 24 September 2021 / Revised: 16 October 2021 / Accepted: 25 October 2021 / Published: 27 October 2021
(This article belongs to the Special Issue Bioinformatics Analysis for Diseases)
Disease gene prediction is to date one of the main computational challenges of precision medicine. It is still uncertain if disease genes have unique functional properties that distinguish them from other non-disease genes or, from a network perspective, if they are located randomly in the interactome or show specific patterns in the network topology. In this study, we propose a new method for disease gene prediction based on the use of biological knowledge-bases (gene-disease associations, genes functional annotations, etc.) and interactome network topology. The proposed algorithm called MOSES is based on the definition of two somewhat opposing sets of genes both disease-specific from different perspectives: warm seeds (i.e., disease genes obtained from databases) and cold seeds (genes far from the disease genes on the interactome and not involved in their biological functions). The application of MOSES to a set of 40 diseases showed that the suggested putative disease genes are significantly enriched in their reference disease. Reassuringly, known and predicted disease genes together, tend to form a connected network module on the human interactome, mitigating the scattered distribution of disease genes which is probably due to both the paucity of disease-gene associations and the incompleteness of the interactome. View Full-Text
Keywords: disease gene prediction; data integration; precision medicine; computational biology disease gene prediction; data integration; precision medicine; computational biology
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MDPI and ACS Style

Petti, M.; Farina, L.; Francone, F.; Lucidi, S.; Macali, A.; Palagi, L.; De Santis, M. MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction. Genes 2021, 12, 1713. https://doi.org/10.3390/genes12111713

AMA Style

Petti M, Farina L, Francone F, Lucidi S, Macali A, Palagi L, De Santis M. MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction. Genes. 2021; 12(11):1713. https://doi.org/10.3390/genes12111713

Chicago/Turabian Style

Petti, Manuela, Lorenzo Farina, Federico Francone, Stefano Lucidi, Amalia Macali, Laura Palagi, and Marianna De Santis. 2021. "MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction" Genes 12, no. 11: 1713. https://doi.org/10.3390/genes12111713

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