Unveiling new disease, pathway, and gene associations via multi-scale neural network
Visualitza/Obre
10.1371/journal.pone.0231059
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/187905
Tipus de documentArticle
Data publicació2020-04-06
EditorPLOS
Condicions d'accésAccés obert
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continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement 3.0 Espanya
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.
CitacióGaudelet, T. [et al.]. Unveiling new disease, pathway, and gene associations via multi-scale neural network. "PLos ONE", 6 Abril 2020, vol. 15, núm. 4, e0231059.
ISSN1932-6203
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