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

Barbara Di Camillo, Fatima Sanchez-Cabo, Gianna Toffolo, K Sreekumaran Nair, Zlatko Trajanoski, Claudio Cobelli

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Article numberS11
JournalBMC Bioinformatics
Volume6
Issue numberSUPPL.4
DOIs
StatePublished - Dec 1 2005

Fingerprint

Microarrays
Microarray
Time series
Quantization
Reverse engineering
Genes
Bayesian networks
Optimization
Dynamic Bayesian Networks
Reverse Engineering
Gene
Sorting
Gene expression
Functional Genomics
Gene Expression Profile
Discrete Data
Significance level
Regulatory Networks
Synthetic Data
Genomics

ASJC Scopus subject areas

  • Medicine(all)
  • Structural Biology
  • Applied Mathematics

Cite this

Di Camillo, B., Sanchez-Cabo, F., Toffolo, G., Nair, K. S., Trajanoski, Z., & Cobelli, C. (2005). A quantization method based on threshold optimization for microarray short time series. BMC Bioinformatics, 6(SUPPL.4), [S11]. https://doi.org/10.1186/1471-2105-6-S4-S11

A quantization method based on threshold optimization for microarray short time series. / Di Camillo, Barbara; Sanchez-Cabo, Fatima; Toffolo, Gianna; Nair, K Sreekumaran; Trajanoski, Zlatko; Cobelli, Claudio.

In: BMC Bioinformatics, Vol. 6, No. SUPPL.4, S11, 01.12.2005.

Research output: Contribution to journalArticle

Di Camillo, B, Sanchez-Cabo, F, Toffolo, G, Nair, KS, Trajanoski, Z & Cobelli, C 2005, 'A quantization method based on threshold optimization for microarray short time series', BMC Bioinformatics, vol. 6, no. SUPPL.4, S11. https://doi.org/10.1186/1471-2105-6-S4-S11
Di Camillo, Barbara ; Sanchez-Cabo, Fatima ; Toffolo, Gianna ; Nair, K Sreekumaran ; Trajanoski, Zlatko ; Cobelli, Claudio. / A quantization method based on threshold optimization for microarray short time series. In: BMC Bioinformatics. 2005 ; Vol. 6, No. SUPPL.4.
@article{e3805ac6ecdb40f28a128088017fe237,
title = "A quantization method based on threshold optimization for microarray short time series",
abstract = "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.",
author = "{Di Camillo}, Barbara and Fatima Sanchez-Cabo and Gianna Toffolo and Nair, {K Sreekumaran} and Zlatko Trajanoski and Claudio Cobelli",
year = "2005",
month = "12",
day = "1",
doi = "10.1186/1471-2105-6-S4-S11",
language = "English (US)",
volume = "6",
journal = "BMC Bioinformatics",
issn = "1471-2105",
publisher = "BioMed Central",
number = "SUPPL.4",

}

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, K Sreekumaran

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

VL - 6

JO - BMC Bioinformatics

JF - BMC Bioinformatics

SN - 1471-2105

IS - SUPPL.4

M1 - S11

ER -