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

deep learning aging

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#1 Engadin

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Posted 19 December 2019 - 07:31 PM


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F U L L   T E X T   S O U R C E (.PDF) :   Oxford Academic_Bioinformatics

 

 

 

 

 

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, 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 (LR) 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.
 
 
 
 
 
 
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Also tagged with one or more of these keywords: deep learning, aging

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