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Unveiling new disease, pathway, and gene associations via multi-scale neural network

Gaudelet, T; Malod-Dognin, N; Sánchez-Valle, J; Pancaldi, V; Valencia, A; Pržulj, N; (2020) Unveiling new disease, pathway, and gene associations via multi-scale neural network. PLoS One , 15 (4) , Article e0231059. 10.1371/journal.pone.0231059. Green open access

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Abstract

Diseases involve complex modifications to the cellular machinery. The gene expression profile of the affected cells contains characteristic patterns linked to a disease. Hence, new biological knowledge about a disease can be extracted from these profiles, improving our ability to diagnose and assess disease risks. This knowledge can be used for drug re-purposing, or by physicians to evaluate a patient’s condition and co-morbidity risk. Here, we consider differential gene expressions obtained by microarray technology for patients diagnosed with various diseases. Based on these data and cellular multi-scale organization, we aim at uncovering disease–disease, disease–gene and disease–pathway associations. We propose a neural network with structure based on the multi-scale organization of proteins in a cell into biological pathways. We show that this model is able to correctly predict the diagnosis for the majority of patients. Through the analysis of the trained model, we predict disease–disease, disease–pathway, and disease–gene associations and validate the predictions by comparisons to known interactions and literature search, proposing putative explanations for the predictions.

Type: Article
Title: Unveiling new disease, pathway, and gene associations via multi-scale neural network
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pone.0231059
Publisher version: https://doi.org/10.1371/journal.pone.0231059
Language: English
Additional information: © 2020 Gaudelet et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. http://creativecommons.org/licenses/by/4.0/
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10096347
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