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Predicting candidate genes from phenotypes, functions and anatomical site of expression

MOTIVATION: Over the past years, many computational methods have been developed to incorporate information about phenotypes for disease–gene prioritization task. These methods generally compute the similarity between a patient’s phenotypes and a database of gene-phenotype to find the most phenotypic...

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Detalles Bibliográficos
Autores principales: Chen, Jun, Althagafi, Azza, Hoehndorf, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8248315/
https://www.ncbi.nlm.nih.gov/pubmed/33051643
http://dx.doi.org/10.1093/bioinformatics/btaa879
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author Chen, Jun
Althagafi, Azza
Hoehndorf, Robert
author_facet Chen, Jun
Althagafi, Azza
Hoehndorf, Robert
author_sort Chen, Jun
collection PubMed
description MOTIVATION: Over the past years, many computational methods have been developed to incorporate information about phenotypes for disease–gene prioritization task. These methods generally compute the similarity between a patient’s phenotypes and a database of gene-phenotype to find the most phenotypically similar match. The main limitation in these methods is their reliance on knowledge about phenotypes associated with particular genes, which is not complete in humans as well as in many model organisms, such as the mouse and fish. Information about functions of gene products and anatomical site of gene expression is available for more genes and can also be related to phenotypes through ontologies and machine-learning models. RESULTS: We developed a novel graph-based machine-learning method for biomedical ontologies, which is able to exploit axioms in ontologies and other graph-structured data. Using our machine-learning method, we embed genes based on their associated phenotypes, functions of the gene products and anatomical location of gene expression. We then develop a machine-learning model to predict gene–disease associations based on the associations between genes and multiple biomedical ontologies, and this model significantly improves over state-of-the-art methods. Furthermore, we extend phenotype-based gene prioritization methods significantly to all genes, which are associated with phenotypes, functions or site of expression. AVAILABILITY AND IMPLEMENTATION: Software and data are available at https://github.com/bio-ontology-research-group/DL2Vec. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-82483152021-07-02 Predicting candidate genes from phenotypes, functions and anatomical site of expression Chen, Jun Althagafi, Azza Hoehndorf, Robert Bioinformatics Original Papers MOTIVATION: Over the past years, many computational methods have been developed to incorporate information about phenotypes for disease–gene prioritization task. These methods generally compute the similarity between a patient’s phenotypes and a database of gene-phenotype to find the most phenotypically similar match. The main limitation in these methods is their reliance on knowledge about phenotypes associated with particular genes, which is not complete in humans as well as in many model organisms, such as the mouse and fish. Information about functions of gene products and anatomical site of gene expression is available for more genes and can also be related to phenotypes through ontologies and machine-learning models. RESULTS: We developed a novel graph-based machine-learning method for biomedical ontologies, which is able to exploit axioms in ontologies and other graph-structured data. Using our machine-learning method, we embed genes based on their associated phenotypes, functions of the gene products and anatomical location of gene expression. We then develop a machine-learning model to predict gene–disease associations based on the associations between genes and multiple biomedical ontologies, and this model significantly improves over state-of-the-art methods. Furthermore, we extend phenotype-based gene prioritization methods significantly to all genes, which are associated with phenotypes, functions or site of expression. AVAILABILITY AND IMPLEMENTATION: Software and data are available at https://github.com/bio-ontology-research-group/DL2Vec. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-10-14 /pmc/articles/PMC8248315/ /pubmed/33051643 http://dx.doi.org/10.1093/bioinformatics/btaa879 Text en © The Author(s) 2020. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
Chen, Jun
Althagafi, Azza
Hoehndorf, Robert
Predicting candidate genes from phenotypes, functions and anatomical site of expression
title Predicting candidate genes from phenotypes, functions and anatomical site of expression
title_full Predicting candidate genes from phenotypes, functions and anatomical site of expression
title_fullStr Predicting candidate genes from phenotypes, functions and anatomical site of expression
title_full_unstemmed Predicting candidate genes from phenotypes, functions and anatomical site of expression
title_short Predicting candidate genes from phenotypes, functions and anatomical site of expression
title_sort predicting candidate genes from phenotypes, functions and anatomical site of expression
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8248315/
https://www.ncbi.nlm.nih.gov/pubmed/33051643
http://dx.doi.org/10.1093/bioinformatics/btaa879
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