Unknown

Dataset Information

0

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


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. AVAILABILITY AND IMPLEMENTATION:The source code and datasets can be found at: https://github.com/fabiofabris/Bioinfo2019. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Fabris F 

PROVIDER: S-EPMC7141856 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

altmetric image

Publications

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

Fabris Fabio F   Palmer Daniel D   Salama Khalid M KM   de Magalhães João Pedro JP   Freitas Alex A AA  

Bioinformatics (Oxford, England) 20200401 7


<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 pat  ...[more]

Similar Datasets

| S-EPMC7453007 | biostudies-literature
| S-EPMC9966505 | biostudies-literature
| S-EPMC8324084 | biostudies-literature
| S-EPMC8416767 | biostudies-literature
| S-EPMC7153739 | biostudies-literature
| S-EPMC6881321 | biostudies-literature
| S-EPMC7499261 | biostudies-literature
| S-EPMC10702052 | biostudies-literature
| S-EPMC6684608 | biostudies-literature
| S-EPMC8021919 | biostudies-literature