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Di Camillo, Barbara - Sanchez-Cabo, Fatima - Toffolo, Gianna - Nair, Sreekumaran K - Trajanoski, Zlatko - Cobelli, Claudio (2005) A quantization method based on threshold optimization for microarray short time series. [Articolo di periodico (online)]

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Per gentile concessione di: http://www.biomedcentral.com/1471-2105/6/S4/S11

Abstract (inglese)

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.


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Tipo di EPrint:Articolo di periodico (online)
Anno di Pubblicazione:2005
Parole chiave (italiano / inglese):microarray, functional genomics
Settori scientifico-disciplinari MIUR:Area 06 - Scienze mediche > MED/03 Genetica medica
Struttura di riferimento:Dipartimenti > Dipartimento di Ingegneria dell'Informazione
Codice ID:1224
Depositato il:09 Dic 2008
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