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Constrained mixture estimation for analysis and robust classification of clinical time series

Motivation: Personalized medicine based on molecular aspects of diseases, such as gene expression profiling, has become increasingly popular. However, one faces multiple challenges when analyzing clinical gene expression data; most of the well-known theoretical issues such as high dimension of featu...

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Autores principales: Costa, Ivan G., Schönhuth, Alexander, Hafemeister, Christoph, Schliep, Alexander
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2687976/
https://www.ncbi.nlm.nih.gov/pubmed/19478017
http://dx.doi.org/10.1093/bioinformatics/btp222
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author Costa, Ivan G.
Schönhuth, Alexander
Hafemeister, Christoph
Schliep, Alexander
author_facet Costa, Ivan G.
Schönhuth, Alexander
Hafemeister, Christoph
Schliep, Alexander
author_sort Costa, Ivan G.
collection PubMed
description Motivation: Personalized medicine based on molecular aspects of diseases, such as gene expression profiling, has become increasingly popular. However, one faces multiple challenges when analyzing clinical gene expression data; most of the well-known theoretical issues such as high dimension of feature spaces versus few examples, noise and missing data apply. Special care is needed when designing classification procedures that support personalized diagnosis and choice of treatment. Here, we particularly focus on classification of interferon-β (IFNβ) treatment response in Multiple Sclerosis (MS) patients which has attracted substantial attention in the recent past. Half of the patients remain unaffected by IFNβ treatment, which is still the standard. For them the treatment should be timely ceased to mitigate the side effects. Results: We propose constrained estimation of mixtures of hidden Markov models as a methodology to classify patient response to IFNβ treatment. The advantages of our approach are that it takes the temporal nature of the data into account and its robustness with respect to noise, missing data and mislabeled samples. Moreover, mixture estimation enables to explore the presence of response sub-groups of patients on the transcriptional level. We clearly outperformed all prior approaches in terms of prediction accuracy, raising it, for the first time, >90%. Additionally, we were able to identify potentially mislabeled samples and to sub-divide the good responders into two sub-groups that exhibited different transcriptional response programs. This is supported by recent findings on MS pathology and therefore may raise interesting clinical follow-up questions. Availability: The method is implemented in the GQL framework and is available at http://www.ghmm.org/gql. Datasets are available at http://www.cin.ufpe.br/∼igcf/MSConst Contact: igcf@cin.ufpe.br Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-26879762009-06-02 Constrained mixture estimation for analysis and robust classification of clinical time series Costa, Ivan G. Schönhuth, Alexander Hafemeister, Christoph Schliep, Alexander Bioinformatics Ismb/Eccb 2009 Conference Proceedings June 27 to July 2, 2009, Stockholm, Sweden Motivation: Personalized medicine based on molecular aspects of diseases, such as gene expression profiling, has become increasingly popular. However, one faces multiple challenges when analyzing clinical gene expression data; most of the well-known theoretical issues such as high dimension of feature spaces versus few examples, noise and missing data apply. Special care is needed when designing classification procedures that support personalized diagnosis and choice of treatment. Here, we particularly focus on classification of interferon-β (IFNβ) treatment response in Multiple Sclerosis (MS) patients which has attracted substantial attention in the recent past. Half of the patients remain unaffected by IFNβ treatment, which is still the standard. For them the treatment should be timely ceased to mitigate the side effects. Results: We propose constrained estimation of mixtures of hidden Markov models as a methodology to classify patient response to IFNβ treatment. The advantages of our approach are that it takes the temporal nature of the data into account and its robustness with respect to noise, missing data and mislabeled samples. Moreover, mixture estimation enables to explore the presence of response sub-groups of patients on the transcriptional level. We clearly outperformed all prior approaches in terms of prediction accuracy, raising it, for the first time, >90%. Additionally, we were able to identify potentially mislabeled samples and to sub-divide the good responders into two sub-groups that exhibited different transcriptional response programs. This is supported by recent findings on MS pathology and therefore may raise interesting clinical follow-up questions. Availability: The method is implemented in the GQL framework and is available at http://www.ghmm.org/gql. Datasets are available at http://www.cin.ufpe.br/∼igcf/MSConst Contact: igcf@cin.ufpe.br Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2009-06-15 2009-05-27 /pmc/articles/PMC2687976/ /pubmed/19478017 http://dx.doi.org/10.1093/bioinformatics/btp222 Text en © 2009 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Ismb/Eccb 2009 Conference Proceedings June 27 to July 2, 2009, Stockholm, Sweden
Costa, Ivan G.
Schönhuth, Alexander
Hafemeister, Christoph
Schliep, Alexander
Constrained mixture estimation for analysis and robust classification of clinical time series
title Constrained mixture estimation for analysis and robust classification of clinical time series
title_full Constrained mixture estimation for analysis and robust classification of clinical time series
title_fullStr Constrained mixture estimation for analysis and robust classification of clinical time series
title_full_unstemmed Constrained mixture estimation for analysis and robust classification of clinical time series
title_short Constrained mixture estimation for analysis and robust classification of clinical time series
title_sort constrained mixture estimation for analysis and robust classification of clinical time series
topic Ismb/Eccb 2009 Conference Proceedings June 27 to July 2, 2009, Stockholm, Sweden
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2687976/
https://www.ncbi.nlm.nih.gov/pubmed/19478017
http://dx.doi.org/10.1093/bioinformatics/btp222
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