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Feature selection based on differentially correlated gene pairs reveals the mechanism of IFN-β therapy for multiple sclerosis
Multiple sclerosis (MS) is one of the most common neurological disabilities of the central nervous system. Immune-modulatory therapy with Interferon-β (IFN-β) is a commonly used first-line treatment to prevent MS patients from relapses. Nevertheless, a large proportion of MS patients on IFN-β therap...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7081782/ https://www.ncbi.nlm.nih.gov/pubmed/32211244 http://dx.doi.org/10.7717/peerj.8812 |
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author | Jin, Tao Wang, Chi Tian, Suyan |
author_facet | Jin, Tao Wang, Chi Tian, Suyan |
author_sort | Jin, Tao |
collection | PubMed |
description | Multiple sclerosis (MS) is one of the most common neurological disabilities of the central nervous system. Immune-modulatory therapy with Interferon-β (IFN-β) is a commonly used first-line treatment to prevent MS patients from relapses. Nevertheless, a large proportion of MS patients on IFN-β therapy experience their first relapse within 2 years of treatment initiation. Feature selection, a machine learning strategy, is routinely used in the fields of bioinformatics and computational biology to determine which subset of genes is most relevant to an outcome of interest. The majority of feature selection methods focus on alterations in gene expression levels. In this study, we sought to determine which genes are most relevant to relapse of MS patients on IFN-β therapy. Rather than the usual focus on alterations in gene expression levels, we devised a feature selection method based on alterations in gene-to-gene interactions. In this study, we applied the proposed method to a longitudinal microarray dataset and evaluated the IFN-β effect on MS patients to identify gene pairs with differentially correlated edges that are consistent over time in the responder group compared to the non-responder group. The resulting gene list had a good predictive ability on an independent validation set and explicit biological implications related to MS. To conclude, it is anticipated that the proposed method will gain widespread interest and application in personalized treatment research to facilitate prediction of which patients may respond to a specific regimen. |
format | Online Article Text |
id | pubmed-7081782 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-70817822020-03-24 Feature selection based on differentially correlated gene pairs reveals the mechanism of IFN-β therapy for multiple sclerosis Jin, Tao Wang, Chi Tian, Suyan PeerJ Bioinformatics Multiple sclerosis (MS) is one of the most common neurological disabilities of the central nervous system. Immune-modulatory therapy with Interferon-β (IFN-β) is a commonly used first-line treatment to prevent MS patients from relapses. Nevertheless, a large proportion of MS patients on IFN-β therapy experience their first relapse within 2 years of treatment initiation. Feature selection, a machine learning strategy, is routinely used in the fields of bioinformatics and computational biology to determine which subset of genes is most relevant to an outcome of interest. The majority of feature selection methods focus on alterations in gene expression levels. In this study, we sought to determine which genes are most relevant to relapse of MS patients on IFN-β therapy. Rather than the usual focus on alterations in gene expression levels, we devised a feature selection method based on alterations in gene-to-gene interactions. In this study, we applied the proposed method to a longitudinal microarray dataset and evaluated the IFN-β effect on MS patients to identify gene pairs with differentially correlated edges that are consistent over time in the responder group compared to the non-responder group. The resulting gene list had a good predictive ability on an independent validation set and explicit biological implications related to MS. To conclude, it is anticipated that the proposed method will gain widespread interest and application in personalized treatment research to facilitate prediction of which patients may respond to a specific regimen. PeerJ Inc. 2020-03-16 /pmc/articles/PMC7081782/ /pubmed/32211244 http://dx.doi.org/10.7717/peerj.8812 Text en © 2020 Jin et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Jin, Tao Wang, Chi Tian, Suyan Feature selection based on differentially correlated gene pairs reveals the mechanism of IFN-β therapy for multiple sclerosis |
title | Feature selection based on differentially correlated gene pairs reveals the mechanism of IFN-β therapy for multiple sclerosis |
title_full | Feature selection based on differentially correlated gene pairs reveals the mechanism of IFN-β therapy for multiple sclerosis |
title_fullStr | Feature selection based on differentially correlated gene pairs reveals the mechanism of IFN-β therapy for multiple sclerosis |
title_full_unstemmed | Feature selection based on differentially correlated gene pairs reveals the mechanism of IFN-β therapy for multiple sclerosis |
title_short | Feature selection based on differentially correlated gene pairs reveals the mechanism of IFN-β therapy for multiple sclerosis |
title_sort | feature selection based on differentially correlated gene pairs reveals the mechanism of ifn-β therapy for multiple sclerosis |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7081782/ https://www.ncbi.nlm.nih.gov/pubmed/32211244 http://dx.doi.org/10.7717/peerj.8812 |
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