A clustering based method accelerating gene regulatory network reconstruction

Georgios N. Dimitrakopoulos, Ioannis A. Maraziotis, Kyriakos Sgarbas, Anastasios Bezerianos

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

Abstract

One important direction of Systems Biology is to infer Gene Regulatory Networks and many methods have been developed recently, but they cannot be applied effectively in full scale data. In this work we propose a framework based on clustering to handle the large dimensionality of the data, aiming to improve accuracy of inferred network while reducing time complexity. We explored the efficiency of this framework employing the newly proposed metric Maximal Information Coefficient (MIC), which showed superior performance in comparison to other well established methods. Utilizing both benchmark and real life datasets, we showed that our method is able to deliver accurate results in fractions of time required by other state of the art methods. Our method provides as output interactions among groups of highly correlated genes, which in an application on an aging experiment were able to reveal aging related pathways.

Original languageEnglish (US)
Title of host publicationProcedia Computer Science
PublisherElsevier
Pages1993-2002
Number of pages10
Volume29
DOIs
StatePublished - 2014
Externally publishedYes
Event14th Annual International Conference on Computational Science, ICCS 2014 - Cairns, QLD, Australia
Duration: Jun 10 2014Jun 12 2014

Other

Other14th Annual International Conference on Computational Science, ICCS 2014
CountryAustralia
CityCairns, QLD
Period6/10/146/12/14

Fingerprint

Genes
Aging of materials
Experiments
Systems Biology

Keywords

  • Clustering
  • Gene regulatory network
  • Maximal information coefficient

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Dimitrakopoulos, G. N., Maraziotis, I. A., Sgarbas, K., & Bezerianos, A. (2014). A clustering based method accelerating gene regulatory network reconstruction. In Procedia Computer Science (Vol. 29, pp. 1993-2002). Elsevier. https://doi.org/10.1016/j.procs.2014.05.183

A clustering based method accelerating gene regulatory network reconstruction. / Dimitrakopoulos, Georgios N.; Maraziotis, Ioannis A.; Sgarbas, Kyriakos; Bezerianos, Anastasios.

Procedia Computer Science. Vol. 29 Elsevier, 2014. p. 1993-2002.

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

Dimitrakopoulos, GN, Maraziotis, IA, Sgarbas, K & Bezerianos, A 2014, A clustering based method accelerating gene regulatory network reconstruction. in Procedia Computer Science. vol. 29, Elsevier, pp. 1993-2002, 14th Annual International Conference on Computational Science, ICCS 2014, Cairns, QLD, Australia, 6/10/14. https://doi.org/10.1016/j.procs.2014.05.183
Dimitrakopoulos GN, Maraziotis IA, Sgarbas K, Bezerianos A. A clustering based method accelerating gene regulatory network reconstruction. In Procedia Computer Science. Vol. 29. Elsevier. 2014. p. 1993-2002 https://doi.org/10.1016/j.procs.2014.05.183
Dimitrakopoulos, Georgios N. ; Maraziotis, Ioannis A. ; Sgarbas, Kyriakos ; Bezerianos, Anastasios. / A clustering based method accelerating gene regulatory network reconstruction. Procedia Computer Science. Vol. 29 Elsevier, 2014. pp. 1993-2002
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