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Alignment and classification of time series gene expression in clinical studies
Motivation: Classification of tissues using static gene-expression data has received considerable attention. Recently, a growing number of expression datasets are measured as a time series. Methods that are specifically designed for this temporal data can both utilize its unique features (temporal e...
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Formato: | Texto |
Lenguaje: | English |
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Oxford University Press
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2718630/ https://www.ncbi.nlm.nih.gov/pubmed/18586707 http://dx.doi.org/10.1093/bioinformatics/btn152 |
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author | Lin, Tien-ho Kaminski, Naftali Bar-Joseph, Ziv |
author_facet | Lin, Tien-ho Kaminski, Naftali Bar-Joseph, Ziv |
author_sort | Lin, Tien-ho |
collection | PubMed |
description | Motivation: Classification of tissues using static gene-expression data has received considerable attention. Recently, a growing number of expression datasets are measured as a time series. Methods that are specifically designed for this temporal data can both utilize its unique features (temporal evolution of profiles) and address its unique challenges (different response rates of patients in the same class). Results: We present a method that utilizes hidden Markov models (HMMs) for the classification task. We use HMMs with less states than time points leading to an alignment of the different patient response rates. To focus on the differences between the two classes we develop a discriminative HMM classifier. Unlike the traditional generative HMM, discriminative HMM can use examples from both classes when learning the model for a specific class. We have tested our method on both simulated and real time series expression data. As we show, our method improves upon prior methods and can suggest markers for specific disease and response stages that are not found when using traditional classifiers. Availability: Matlab implementation is available from http://www.cs.cmu.edu/~thlin/tram/ Contact: zivbj@cs.cmu.edu |
format | Text |
id | pubmed-2718630 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-27186302009-07-31 Alignment and classification of time series gene expression in clinical studies Lin, Tien-ho Kaminski, Naftali Bar-Joseph, Ziv Bioinformatics Ismb 2008 Conference Proceedings 19–23 July 2008, Toronto Motivation: Classification of tissues using static gene-expression data has received considerable attention. Recently, a growing number of expression datasets are measured as a time series. Methods that are specifically designed for this temporal data can both utilize its unique features (temporal evolution of profiles) and address its unique challenges (different response rates of patients in the same class). Results: We present a method that utilizes hidden Markov models (HMMs) for the classification task. We use HMMs with less states than time points leading to an alignment of the different patient response rates. To focus on the differences between the two classes we develop a discriminative HMM classifier. Unlike the traditional generative HMM, discriminative HMM can use examples from both classes when learning the model for a specific class. We have tested our method on both simulated and real time series expression data. As we show, our method improves upon prior methods and can suggest markers for specific disease and response stages that are not found when using traditional classifiers. Availability: Matlab implementation is available from http://www.cs.cmu.edu/~thlin/tram/ Contact: zivbj@cs.cmu.edu Oxford University Press 2008-07-01 /pmc/articles/PMC2718630/ /pubmed/18586707 http://dx.doi.org/10.1093/bioinformatics/btn152 Text en © 2008 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 2008 Conference Proceedings 19–23 July 2008, Toronto Lin, Tien-ho Kaminski, Naftali Bar-Joseph, Ziv Alignment and classification of time series gene expression in clinical studies |
title | Alignment and classification of time series gene expression in clinical studies |
title_full | Alignment and classification of time series gene expression in clinical studies |
title_fullStr | Alignment and classification of time series gene expression in clinical studies |
title_full_unstemmed | Alignment and classification of time series gene expression in clinical studies |
title_short | Alignment and classification of time series gene expression in clinical studies |
title_sort | alignment and classification of time series gene expression in clinical studies |
topic | Ismb 2008 Conference Proceedings 19–23 July 2008, Toronto |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2718630/ https://www.ncbi.nlm.nih.gov/pubmed/18586707 http://dx.doi.org/10.1093/bioinformatics/btn152 |
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