Inferring gene expression networks via static and dynamic data integration

Fulvia Ferrazzi, Paolo Magni, Lucia Sacchi, Riccardo Bellazzi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a novel approach for the extraction of gene regulatory networks from DNA microarray data. The approach is characterized by the integration of data coming from static and dynamic experiments, exploiting also prior knowledge on the biological process under analysis. A starting network topology is built by analyzing gene expression data measured during knockout experiments. The analysis of time series expression profiles allows to derive the complete network structure and to learn a model of the gene expression dynamics: to this aim a genetic algorithm search coupled with a regression model of the gene interactions is exploited. The method has been applied to the reconstruction of a network of genes involved into the Saccharomyces Cerevisiae cell cycle. The proposed approach was able to reconstruct known relationships among genes and to provide meaningful biological results.

Original languageEnglish
Title of host publicationStudies in Health Technology and Informatics
Pages119-124
Number of pages6
Volume124
Publication statusPublished - 2006
Event20th International Congress of the European Federation for Medical Informatics, MIE 2006 - Maastricht, Netherlands
Duration: Aug 27 2006Aug 30 2006

Other

Other20th International Congress of the European Federation for Medical Informatics, MIE 2006
Country/TerritoryNetherlands
CityMaastricht
Period8/27/068/30/06

Keywords

  • DNA microarrays
  • machine learning
  • systems biology

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management
  • Medicine(all)

Fingerprint

Dive into the research topics of 'Inferring gene expression networks via static and dynamic data integration'. Together they form a unique fingerprint.

Cite this