Precedence temporal networks from gene expression data

Lucia Sacchi, Riccardo Bellazzi, Riccardo Porreca, Cristiana Larizza, Paolo Magni

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

Abstract

In this paper we introduce a novel method to extract from data and graphically represent the temporal relationships between events, called Precedence Temporal Network. The new approach first derives events from time series by exploiting the temporal abstraction technique, then derives temporal precedence between abstractions in terms of association rules and finally expresses the relationships as a labeled graph. The method is applied to the problem of representing the temporal behavior of gene expressions, as they are collected by DNA microarrays. In particular, in this paper we present the results obtained from the analysis of the expression of a subset of the genes involved in cell-cycle regulation.

Original languageEnglish
Title of host publicationProceedings - IEEE Symposium on Computer-Based Medical Systems
Pages109-114
Number of pages6
Publication statusPublished - 2005
Event18th IEEE Symposium on Computer-Based Medical Systems - Dublin, Ireland, United Kingdom
Duration: Jun 23 2005Jun 24 2005

Other

Other18th IEEE Symposium on Computer-Based Medical Systems
CountryUnited Kingdom
CityDublin, Ireland
Period6/23/056/24/05

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Precedence temporal networks from gene expression data'. Together they form a unique fingerprint.

  • Cite this

    Sacchi, L., Bellazzi, R., Porreca, R., Larizza, C., & Magni, P. (2005). Precedence temporal networks from gene expression data. In Proceedings - IEEE Symposium on Computer-Based Medical Systems (pp. 109-114)