Gene classification and regulatory prediction based on transcriptional modeling

Ilias Tagkopoulos, Dimitrios Serpanos

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

Abstract

We present a methodology that aims to elucidate regulatory mechanisms by grouping together genes which share the same regulatory network. In our method, we use multi-state partition functions and thermodynamic models to derive six distinct correlation classes that correspond to various ProteinProtein and Protein-DNA interactions. We then introduce a novel biclustering algorithm that clusters together genes whose expression profiles exhibit the derived correlations in various conditions. The functional enrichment and statistical significance of the resulting clusters is evaluated by precision-recall curves and calculated p-values. Moreover, we analyzed the upstream regions of all genes that comprise each cluster, in order to verify that the derived correlation classes capture the expression of genes with common regulation. We have been able to identify over hundred strongly conserved sequences, among which eight match well-known regulatory motifs. Finally, further analysis of the identified conserved sequences provides not only an explanation of the classification performance, but serves also as an indicator of the regulatory coherence of various groups.

Original languageEnglish
Title of host publicationProceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology
Pages29-34
Number of pages6
Volume2005
DOIs
Publication statusPublished - 1 Dec 2005
Externally publishedYes
Event5th IEEE International Symposium on Signal Processing and Information Technology - Athens, Greece
Duration: 18 Dec 200521 Dec 2005

Other

Other5th IEEE International Symposium on Signal Processing and Information Technology
CountryGreece
CityAthens
Period18/12/0521/12/05

Fingerprint

Genes
Gene expression
DNA
Thermodynamics
Proteins

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Tagkopoulos, I., & Serpanos, D. (2005). Gene classification and regulatory prediction based on transcriptional modeling. In Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology (Vol. 2005, pp. 29-34). [1577065] https://doi.org/10.1109/ISSPIT.2005.1577065

Gene classification and regulatory prediction based on transcriptional modeling. / Tagkopoulos, Ilias; Serpanos, Dimitrios.

Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology. Vol. 2005 2005. p. 29-34 1577065.

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

Tagkopoulos, I & Serpanos, D 2005, Gene classification and regulatory prediction based on transcriptional modeling. in Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology. vol. 2005, 1577065, pp. 29-34, 5th IEEE International Symposium on Signal Processing and Information Technology, Athens, Greece, 18/12/05. https://doi.org/10.1109/ISSPIT.2005.1577065
Tagkopoulos I, Serpanos D. Gene classification and regulatory prediction based on transcriptional modeling. In Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology. Vol. 2005. 2005. p. 29-34. 1577065 https://doi.org/10.1109/ISSPIT.2005.1577065
Tagkopoulos, Ilias ; Serpanos, Dimitrios. / Gene classification and regulatory prediction based on transcriptional modeling. Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology. Vol. 2005 2005. pp. 29-34
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