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A categorical network approach for discovering differentially expressed regulations in cancer

BACKGROUND: The problem of efficient utilization of genome-wide expression profiles for identification and prediction of complex disease conditions is both important and challenging. Polygenic pathologies such as most types of cancer involve disregulation of many interacting genes which has prompted...

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Autor principal: Balov, Nikolay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3980263/
https://www.ncbi.nlm.nih.gov/pubmed/24565081
http://dx.doi.org/10.1186/1755-8794-6-S3-S1
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author Balov, Nikolay
author_facet Balov, Nikolay
author_sort Balov, Nikolay
collection PubMed
description BACKGROUND: The problem of efficient utilization of genome-wide expression profiles for identification and prediction of complex disease conditions is both important and challenging. Polygenic pathologies such as most types of cancer involve disregulation of many interacting genes which has prompted search for suitable statistical models for their representation. By accounting for changes in gene regulations between comparable conditions, graphical statistical models are expected to improve prediction precision. METHODS: In comparison problems with two or more experimental conditions, we represent the classes by categorical Bayesian networks that share one and the same graph structure but have class-specific probability parameters. The graph structure is learned by a score-based procedure that maximizes the difference between class probabilities using a suitable measure of divergence. The proposed framework includes an indirect model selection by adhering to a principle of optimal class separation and identifies interactions presenting significant difference between the compared conditions. RESULTS: We evaluate the performance of the new model against some benchmark algorithms such as support vector machine, penalized linear regression and linear Gaussian networks. The classifiers are compared by prediction accuracy across 15 different data sets from breast, lung, gastric and renal cancer studies. In addition to the demonstrated strong performance against the competitors, the proposed method is able to identify disease specific changes in gene regulations which are inaccessible by other approaches. The latter is illustrated by analyzing some gene interactions differentiating adenocarcinoma and squamous cell lung cancers.
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spelling pubmed-39802632014-04-24 A categorical network approach for discovering differentially expressed regulations in cancer Balov, Nikolay BMC Med Genomics Research BACKGROUND: The problem of efficient utilization of genome-wide expression profiles for identification and prediction of complex disease conditions is both important and challenging. Polygenic pathologies such as most types of cancer involve disregulation of many interacting genes which has prompted search for suitable statistical models for their representation. By accounting for changes in gene regulations between comparable conditions, graphical statistical models are expected to improve prediction precision. METHODS: In comparison problems with two or more experimental conditions, we represent the classes by categorical Bayesian networks that share one and the same graph structure but have class-specific probability parameters. The graph structure is learned by a score-based procedure that maximizes the difference between class probabilities using a suitable measure of divergence. The proposed framework includes an indirect model selection by adhering to a principle of optimal class separation and identifies interactions presenting significant difference between the compared conditions. RESULTS: We evaluate the performance of the new model against some benchmark algorithms such as support vector machine, penalized linear regression and linear Gaussian networks. The classifiers are compared by prediction accuracy across 15 different data sets from breast, lung, gastric and renal cancer studies. In addition to the demonstrated strong performance against the competitors, the proposed method is able to identify disease specific changes in gene regulations which are inaccessible by other approaches. The latter is illustrated by analyzing some gene interactions differentiating adenocarcinoma and squamous cell lung cancers. BioMed Central 2013-11-11 /pmc/articles/PMC3980263/ /pubmed/24565081 http://dx.doi.org/10.1186/1755-8794-6-S3-S1 Text en Copyright © 2013 Balov; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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
Balov, Nikolay
A categorical network approach for discovering differentially expressed regulations in cancer
title A categorical network approach for discovering differentially expressed regulations in cancer
title_full A categorical network approach for discovering differentially expressed regulations in cancer
title_fullStr A categorical network approach for discovering differentially expressed regulations in cancer
title_full_unstemmed A categorical network approach for discovering differentially expressed regulations in cancer
title_short A categorical network approach for discovering differentially expressed regulations in cancer
title_sort categorical network approach for discovering differentially expressed regulations in cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3980263/
https://www.ncbi.nlm.nih.gov/pubmed/24565081
http://dx.doi.org/10.1186/1755-8794-6-S3-S1
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