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

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 interacto...

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Autores principales:	Manuela Petti, Lorenzo Farina, Federico Francone, Stefano Lucidi, Amalia Macali, Laura Palagi, Marianna De Santis
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	disease gene prediction data integration precision medicine computational biology Genetics QH426-470
Acceso en línea:	https://doaj.org/article/2502c2f80ca84500b21f69315cd1c0ac
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spelling	oai:doaj.org-article:2502c2f80ca84500b21f69315cd1c0ac2021-11-25T17:41:10ZMOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction10.3390/genes121117132073-4425https://doaj.org/article/2502c2f80ca84500b21f69315cd1c0ac2021-10-01T00:00:00Zhttps://www.mdpi.com/2073-4425/12/11/1713https://doaj.org/toc/2073-4425Disease 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.Manuela PettiLorenzo FarinaFederico FranconeStefano LucidiAmalia MacaliLaura PalagiMarianna De SantisMDPI AGarticledisease gene predictiondata integrationprecision medicinecomputational biologyGeneticsQH426-470ENGenes, Vol 12, Iss 1713, p 1713 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	disease gene prediction data integration precision medicine computational biology Genetics QH426-470
spellingShingle	disease gene prediction data integration precision medicine computational biology Genetics QH426-470 Manuela Petti Lorenzo Farina Federico Francone Stefano Lucidi Amalia Macali Laura Palagi Marianna De Santis MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction
description	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.
format	article
author	Manuela Petti Lorenzo Farina Federico Francone Stefano Lucidi Amalia Macali Laura Palagi Marianna De Santis
author_facet	Manuela Petti Lorenzo Farina Federico Francone Stefano Lucidi Amalia Macali Laura Palagi Marianna De Santis
author_sort	Manuela Petti
title	MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction
title_short	MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction
title_full	MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction
title_fullStr	MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction
title_full_unstemmed	MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction
title_sort	moses: a new approach to integrate interactome topology and functional features for disease gene prediction
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2502c2f80ca84500b21f69315cd1c0ac
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MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction

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