Using deep learning to associate human genes with age-related diseases

Fabris, Fabio, Palmer, Daniel, Salama, Khalid M, de Magalhaes, Joao Pedro ORCID: 0000-0002-6363-2465 and Freitas, Alex A (2020) Using deep learning to associate human genes with age-related diseases. BIOINFORMATICS, 36 (7). pp. 2202-2208.

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Official URL: http://dx.doi.org/10.1093/bioinformatics/btz887

Abstract

<h4>Motivation</h4>One way to identify genes possibly associated with ageing is to build a classification model (from the machine learning field) capable of classifying genes as associated with multiple age-related diseases. To build this model, we use a pre-compiled list of human genes associated with age-related diseases and apply a novel Deep Neural Network (DNN) method to find associations between gene descriptors (e.g. Gene Ontology terms, protein-protein interaction data and biological pathway information) and age-related diseases.<h4>Results</h4>The novelty of our new DNN method is its modular architecture, which has the capability of combining several sources of biological data to predict which ageing-related diseases a gene is associated with (if any). Our DNN method achieves better predictive performance than standard DNN approaches, a Gradient Boosted Tree classifier (a strong baseline method) and a Logistic Regression classifier. Given the DNN model produced by our method, we use two approaches to identify human genes that are not known to be associated with age-related diseases according to our dataset. First, we investigate genes that are close to other disease-associated genes in a complex multi-dimensional feature space learned by the DNN algorithm. Second, using the class label probabilities output by our DNN approach, we identify genes with a high probability of being associated with age-related diseases according to the model. We provide evidence of these putative associations retrieved from the DNN model with literature support.<h4>Availability and implementation</h4>The source code and datasets can be found at: https://github.com/fabiofabris/Bioinfo2019.<h4>Supplementary information</h4>Supplementary data are available at Bioinformatics online.

Item Type:	Article
Uncontrolled Keywords:	Humans, Aging, Gene Ontology, Machine Learning, Deep Learning, Neural Networks, Computer
Depositing User:	Symplectic Admin
Date Deposited:	19 Dec 2019 15:44
Last Modified:	19 Jan 2023 00:12
DOI:	10.1093/bioinformatics/btz887
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3067072