(2014). A Clustering based Method Accelerating Gene Regulatory Network Reconstruction. In Procedia Computer Science (pp. 1993–2002). Elsevier BV. https://doi.org/10.1016/j.procs.2014.05.183
Περίληψη
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.
- DOI
- 10.1016/j.procs.2014.05.183
- Τύπος
- Άρθρο σε Πρακτικά Συνεδρίου
- Έτος
- 2014
Σύνδεσμοι
BibTeX
@inproceedings{dimitrakopoulos2014clustering,
title = {A Clustering based Method Accelerating Gene Regulatory Network Reconstruction},
author = {Georgios N. Dimitrakopoulos and Ioannis A. Maraziotis and Kyriakos Sgarbas and Anastasios Bezerianos},
url = {https://doi.org/10.1016/j.procs.2014.05.183},
doi = {10.1016/j.procs.2014.05.183},
year = {2014},
date = {2014-01-01},
booktitle = {Procedia Computer Science},
volume = {29},
pages = {1993–2002},
publisher = {Elsevier BV},
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.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}