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

Fabris, Fabio, Palmer, Daniel, Salama, Khalid M., de Magalhaes, Joao Pedro, Freitas, Alex A. (2019) Using deep learning to associate human genes with age-related diseases. Bioinformatics, . ISSN 1367-4803. (doi:10.1093/bioinformatics/btz887) (KAR id:79932)

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

Abstract

Motivation: 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. Results: 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. The source code and datasets can be found at: https://github.com/fabiofabris/Bioinfo2019.

Item Type: Article
DOI/Identification number: 10.1093/bioinformatics/btz887
Uncontrolled keywords: data mining, machine learning, classification, bioinformatics, ageing, deep learning
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 03 Feb 2020 18:20 UTC
Last Modified: 06 Mar 2020 12:02 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/79932 (The current URI for this page, for reference purposes)
Fabris, Fabio: https://orcid.org/0000-0001-7159-4668
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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