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dcterms:title
Using deep learning to associate human genes with age-related diseases
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2019-12-17
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n9:ext-a.a.freitas@kent.ac.uk n9:ext-a04fdbe50ad8173cddb0a585f2d7c64f n9:ext-5609a485d5552799d7bc23ba30ad3560 n9:ext-dff5adedc92fa310f1a7ebd6f97f2c99 n9:ext-kms39@kent.ac.uk
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bibo: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.
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7
bibo:volume
36