This article is part of the supplement: Italian Society of Bioinformatics (BITS): Annual Meeting 2005

Open Access Research article

A quantization method based on threshold optimization for microarray short time series

Barbara Di Camillo1, Fatima Sanchez-Cabo2, Gianna Toffolo1, Sreekumaran K Nair3, Zlatko Trajanoski2 and Claudio Cobelli1*

Author Affiliations

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

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BMC Bioinformatics 2005, 6(Suppl 4):S11  doi:10.1186/1471-2105-6-S4-S11

Published: 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.