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Network-based prediction of polygenic disease genes involved in cell motility
BACKGROUND: Schizophrenia and autism are examples of polygenic diseases caused by a multitude of genetic variants, many of which are still poorly understood. Recently, both diseases have been associated with disrupted neuron motility and migration patterns, suggesting that aberrant cell motility is...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6584515/ https://www.ncbi.nlm.nih.gov/pubmed/31216978 http://dx.doi.org/10.1186/s12859-019-2834-1 |
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author | Bern, Miriam King, Alexander Applewhite, Derek A. Ritz, Anna |
author_facet | Bern, Miriam King, Alexander Applewhite, Derek A. Ritz, Anna |
author_sort | Bern, Miriam |
collection | PubMed |
description | BACKGROUND: Schizophrenia and autism are examples of polygenic diseases caused by a multitude of genetic variants, many of which are still poorly understood. Recently, both diseases have been associated with disrupted neuron motility and migration patterns, suggesting that aberrant cell motility is a phenotype for these neurological diseases. RESULTS: We formulate the Polygenic Disease Phenotype Problem which seeks to identify candidate disease genes that may be associated with a phenotype such as cell motility. We present a machine learning approach to solve this problem for schizophrenia and autism genes within a brain-specific functional interaction network. Our method outperforms peer semi-supervised learning approaches, achieving better cross-validation accuracy across different sets of gold-standard positives. We identify top candidates for both schizophrenia and autism, and select six genes labeled as schizophrenia positives that are predicted to be associated with cell motility for follow-up experiments. CONCLUSIONS: Candidate genes predicted by our method suggest testable hypotheses about these genes’ role in cell motility regulation, offering a framework for generating predictions for experimental validation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2834-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6584515 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-65845152019-06-26 Network-based prediction of polygenic disease genes involved in cell motility Bern, Miriam King, Alexander Applewhite, Derek A. Ritz, Anna BMC Bioinformatics Research BACKGROUND: Schizophrenia and autism are examples of polygenic diseases caused by a multitude of genetic variants, many of which are still poorly understood. Recently, both diseases have been associated with disrupted neuron motility and migration patterns, suggesting that aberrant cell motility is a phenotype for these neurological diseases. RESULTS: We formulate the Polygenic Disease Phenotype Problem which seeks to identify candidate disease genes that may be associated with a phenotype such as cell motility. We present a machine learning approach to solve this problem for schizophrenia and autism genes within a brain-specific functional interaction network. Our method outperforms peer semi-supervised learning approaches, achieving better cross-validation accuracy across different sets of gold-standard positives. We identify top candidates for both schizophrenia and autism, and select six genes labeled as schizophrenia positives that are predicted to be associated with cell motility for follow-up experiments. CONCLUSIONS: Candidate genes predicted by our method suggest testable hypotheses about these genes’ role in cell motility regulation, offering a framework for generating predictions for experimental validation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2834-1) contains supplementary material, which is available to authorized users. BioMed Central 2019-06-20 /pmc/articles/PMC6584515/ /pubmed/31216978 http://dx.doi.org/10.1186/s12859-019-2834-1 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Bern, Miriam King, Alexander Applewhite, Derek A. Ritz, Anna Network-based prediction of polygenic disease genes involved in cell motility |
title | Network-based prediction of polygenic disease genes involved in cell motility |
title_full | Network-based prediction of polygenic disease genes involved in cell motility |
title_fullStr | Network-based prediction of polygenic disease genes involved in cell motility |
title_full_unstemmed | Network-based prediction of polygenic disease genes involved in cell motility |
title_short | Network-based prediction of polygenic disease genes involved in cell motility |
title_sort | network-based prediction of polygenic disease genes involved in cell motility |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6584515/ https://www.ncbi.nlm.nih.gov/pubmed/31216978 http://dx.doi.org/10.1186/s12859-019-2834-1 |
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