(2014). Supervised method for construction of microRNA-mRNA networks: Application in cardiac tissue aging dataset. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 318–321). IEEE. https://doi.org/10.1109/embc.2014.6943593
Περίληψη
MicroRNAs play an important role in regulation of gene expression, but still detection of their targets remains a challenge. In this work we present a supervised regulatory network inference method with aim to identify potential target genes (mRNAs) of microRNAs. Briefly, the proposed method exploiting mRNA and microRNA expression trains Random Forests on known interactions and subsequently it is able to predict novel ones. In parallel, we incorporate different available data sources, such as Gene Ontology and ProteinProtein Interactions, to deliver biologically consistent results. Application in both benchmark data and an experiment studying aging showed robust performance.
- DOI
- 10.1109/embc.2014.6943593
- Τύπος
- Άρθρο σε Πρακτικά Συνεδρίου
- Έτος
- 2014
Σύνδεσμοι
BibTeX
@inproceedings{dimitrakopoulos2014supervised,
title = {Supervised method for construction of microRNA-mRNA networks: Application in cardiac tissue aging dataset},
author = {Georgios N. Dimitrakopoulos and Konstantina Dimitrakopoulou and Ioannis A. Maraziotis and Kyriakos Sgarbas and Anastasios Bezerianos},
url = {https://doi.org/10.1109/embc.2014.6943593},
doi = {10.1109/embc.2014.6943593},
year = {2014},
date = {2014-01-01},
booktitle = {2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
volume = {7},
pages = {318–321},
publisher = {IEEE},
abstract = {MicroRNAs play an important role in regulation of gene expression, but still detection of their targets remains a challenge. In this work we present a supervised regulatory network inference method with aim to identify potential target genes (mRNAs) of microRNAs. Briefly, the proposed method exploiting mRNA and microRNA expression trains Random Forests on known interactions and subsequently it is able to predict novel ones. In parallel, we incorporate different available data sources, such as Gene Ontology and ProteinProtein Interactions, to deliver biologically consistent results. Application in both benchmark data and an experiment studying aging showed robust performance.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}