TY - JOUR
T1 - A clustering based method accelerating gene regulatory network reconstruction
AU - Dimitrakopoulos, Georgios N.
AU - Maraziotis, Ioannis A.
AU - Sgarbas, Kyriakos
AU - Bezerianos, Anastasios
N1 - Funding Information:
This research has been co-financed by the Eur opean Union (European Social Fund - ESF) and Greek national funds through the Operational Progr am "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: Thalis. Investing in knowledge society through the European Social Fund.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Clustering
KW - Gene regulatory network
KW - Maximal information coefficient
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U2 - 10.1016/j.procs.2014.05.183
DO - 10.1016/j.procs.2014.05.183
M3 - Conference article
AN - SCOPUS:84902835866
SN - 1877-0509
VL - 29
SP - 1993
EP - 2002
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 14th Annual International Conference on Computational Science, ICCS 2014
Y2 - 10 June 2014 through 12 June 2014
ER -