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  Using hidden Markov models to analyze gene expression time course data

Schliep, A., Schönhuth, A., & Steinhoff, C. (2003). Using hidden Markov models to analyze gene expression time course data. Bioinformatics, 19(Supplement 1), i255-i263.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-8A14-4 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-8A15-2
Genre: Conference Paper

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 Creators:
Schliep, Alexander1, Author              
Schönhuth, Alexander, Author
Steinhoff, Christine1, Author              
Affiliations:
1Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433547              

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Free keywords: gene expression; time course; model-based clustering; Hidden Markov Models
 Abstract: Motivation: Cellular processes cause changes over time. Observing and measuring those changes over time allows insights into the how and why of regulation. The experimental platform for doing the appropriate large-scale experiments to obtain time-courses of expression levels is provided by microarray technology. However, the proper way of analyzing the resulting time course data is still very much an issue under investigation. The inherent time dependencies in the data suggest that clustering techniques which reflect those dependencies yield improved performance. Results: We propose to use Hidden Markov Models (HMMs) to account for the horizontal dependencies along the time axis in time course data and to cope with the prevalent errors and missing values. The HMMs are used within a model-based clustering framework. We are given a number of clusters, each represented by one Hidden Markov Model from a finite collection encompassing typical qualitative behavior. Then, our method finds in an iterative procedure cluster models and an assignment of data points to these models that maximizes the joint likelihood of clustering and models. Partially supervised learning—adding groups of labeled data to the initial collection of clusters—is supported. A graphical user interface allows quering an expression profile dataset for time course similar to a prototype graphically defined as a sequence of levels and durations. We also propose a heuristic approach to automate determination of the number of clusters. We evaluate the method on published yeast cell cycle and fibroblasts serum response datasets, and compare them, with favorable results, to the autoregressive curves method.

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Language(s): eng - English
 Dates: 2003-07
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 176223
 Degree: -

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Title: Eleventh International Conference on Intelligent Systems for Molecular Biology (ISMB 2003)
Place of Event: Brisbane, Australia
Start-/End Date: 2003-06-29 - 2003-07-03

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Title: Bioinformatics
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 19 (Supplement 1) Sequence Number: - Start / End Page: i255 - i263 Identifier: ISSN: 1367-4803
ISSN: 1460-2059