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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2021
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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) |
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disease gene prediction data integration precision medicine computational biology Genetics QH426-470 |
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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 |
work_keys_str_mv |
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