A clustering based method accelerating gene regulatory network reconstruction

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

Research output: Contribution to journalConference articlepeer-review

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)
Pages (from-to)1993-2002
Number of pages10
JournalProcedia Computer Science
Volume29
DOIs
StatePublished - Jan 1 2014
Event14th Annual International Conference on Computational Science, ICCS 2014 - Cairns, QLD, Australia
Duration: Jun 10 2014Jun 12 2014

Keywords

  • Clustering
  • Gene regulatory network
  • Maximal information coefficient

ASJC Scopus subject areas

  • Computer Science(all)

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