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An unsupervised conditional random fields approach for clustering gene expression time series

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journal contribution
posted on 2008-01-01, 00:00 authored by Chang-Tsun LiChang-Tsun Li, Y Yuan, R Wilson
Motivation: There is a growing interest in extracting statistical patterns from gene expression time-series data, in which a key challenge is the development of stable and accurate probabilistic models. Currently popular models, however, would be computationally prohibitive unless some independence assumptions are made to describe large-scale data. We propose an unsupervised conditional random fields (CRF) model to overcome this problem by progressively infusing information into the labelling process through a small variable voting pool. Results: An unsupervised CRF model is proposed for efficient analysis of gene expression time series and is successfully applied to gene class discovery and class prediction. The proposed model treats each time series as a random field and assigns an optimal cluster label to each time series, so as to partition the time series into clusters without a priori knowledge about the number of clusters and the initial centroids. Another advantage of the proposed method is the relaxation of independence assumptions. © The Author 2008. Published by Oxford University Press. All rights reserved.

History

Journal

Bioinformatics

Volume

24

Issue

21

Pagination

2467 - 2473

Publisher

Oxford University Press

Location

Oxford, Eng.

ISSN

1367-4803

eISSN

1460-2059

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal