An unsupervised conditional random fields approach for clustering gene expression time series.
BACKGROUND
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.
Li
Chang-Tsun
CT
Department of Computer Science, University of Warwick, Coventry, UK. ctli@dcs.warwick.ac.uk
Yuan
Yinyin
Y
Wilson
Roland
R
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology; D020869:Gene Expression Profiling / Q000379:methods*; D015233:Models, Statistical