This article is part of the supplement: Italian Society of Bioinformatics (BITS): Annual Meeting 2005
A quantization method based on threshold optimization for microarray short time series
1 Information Engineering Department, University of Padova, Padova, 35131 Italy
2 Institute for Genomics and Bioinformatics and Christian Doppler Labor, Graz University of Technology, Graz, 8010 Austria
3 Endocrinology Division, Mayo Clinic, Rochester, Minnesota 55905, USA
BMC Bioinformatics 2005, 6(Suppl 4):S11 doi:10.1186/1471-2105-6-S4-S11Published: 1 December 2005
Reconstructing regulatory networks from gene expression profiles is a challenging problem of functional genomics. In microarray studies the number of samples is often very limited compared to the number of genes, thus the use of discrete data may help reducing the probability of finding random associations between genes.
A quantization method, based on a model of the experimental error and on a significance level able to compromise between false positive and false negative classifications, is presented, which can be used as a preliminary step in discrete reverse engineering methods. The method is tested on continuous synthetic data with two discrete reverse engineering methods: Reveal and Dynamic Bayesian Networks.
The quantization method, evaluated in comparison with two standard methods, 5% threshold based on experimental error and rank sorting, improves the ability of Reveal and Dynamic Bayesian Networks to identify relations among genes.