TY - JOUR
T1 - A quantization method based on threshold optimization for microarray short time series
AU - Di Camillo, Barbara
AU - Sanchez-Cabo, Fatima
AU - Toffolo, Gianna
AU - Nair, Sreekumaran K.
AU - Trajanoski, Zlatko
AU - Cobelli, Claudio
PY - 2005/12/1
Y1 - 2005/12/1
N2 - Background: 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. Results: 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. Conclusion: 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.
AB - Background: 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. Results: 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. Conclusion: 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.
UR - http://www.scopus.com/inward/record.url?scp=33947314171&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33947314171&partnerID=8YFLogxK
U2 - 10.1186/1471-2105-6-S4-S11
DO - 10.1186/1471-2105-6-S4-S11
M3 - Article
C2 - 16351737
AN - SCOPUS:33947314171
SN - 1471-2105
VL - 6
JO - BMC bioinformatics
JF - BMC bioinformatics
IS - SUPPL.4
M1 - S11
ER -