An unsupervised conditional random fields approach for clustering gene expression time series
Li, Chang-Tsun, Yuan, Yinyin and Wilson, Roland. (2008) An unsupervised conditional random fields approach for clustering gene expression time series. Bioinformatics, Volume 24 (Number 21). pp. 2467-2473. ISSN 1367-4803Full text not available from this repository.
Official URL: http://dx.doi.org/10.1093/bioinformatics/btn375
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.
|Item Type:||Journal Article|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Q Science > QH Natural history > QH426 Genetics
|Divisions:||Faculty of Science > Computer Science|
|Library of Congress Subject Headings (LCSH):||Gene expression -- Data processing, Gene expression -- Analysis, Random fields, Time-series analysis -- Computer programs, Microclusters|
|Journal or Publication Title:||Bioinformatics|
|Publisher:||Oxford University Press|
|Official Date:||1 November 2008|
|Number of Pages:||7|
|Page Range:||pp. 2467-2473|
|Access rights to Published version:||Restricted or Subscription Access|
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