دورية أكاديمية
Using deep learning to associate human genes with age-related diseases.
| العنوان: | Using deep learning to associate human genes with age-related diseases. |
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| المؤلفون: | Fabris F; School of Computing, University of Kent, Canterbury, Kent CT2 7NF, UK., Palmer D; Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L7 8TX, UK., Salama KM; School of Computing, University of Kent, Canterbury, Kent CT2 7NF, UK., de Magalhães JP; Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool L7 8TX, UK., Freitas AA; School of Computing, University of Kent, Canterbury, Kent CT2 7NF, UK. |
| المصدر: | Bioinformatics (Oxford, England) [Bioinformatics] 2020 Apr 01; Vol. 36 (7), pp. 2202-2208. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: Oxford University Press Country of Publication: England NLM ID: 9808944 Publication Model: Print Cited Medium: Internet ISSN: 1367-4811 (Electronic) Linking ISSN: 13674803 NLM ISO Abbreviation: Bioinformatics Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Oxford : Oxford University Press, c1998- |
| مواضيع طبية MeSH: | Deep Learning* , Machine Learning*, Aging ; Gene Ontology ; Humans ; Neural Networks, Computer |
| مستخلص: | 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. (© The Author(s) 2019. Published by Oxford University Press.) |
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| معلومات مُعتمدة: | BB/R014949/1 United Kingdom BB_ Biotechnology and Biological Sciences Research Council; 208375/Z/17/Z United Kingdom WT_ Wellcome Trust |
| تواريخ الأحداث: | Date Created: 20191218 Date Completed: 20200916 Latest Revision: 20200916 |
| رمز التحديث: | 20231215 |
| مُعرف محوري في PubMed: | PMC7141856 |
| DOI: | 10.1093/bioinformatics/btz887 |
| PMID: | 31845988 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 1367-4811 |
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| DOI: | 10.1093/bioinformatics/btz887 |