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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 318-321 |
| Number of pages | 4 |
| Journal | Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference |
| Volume | 2014 |
| DOIs | |
| State | Published - 2014 |
| Externally published | Yes |
Fingerprint
ASJC Scopus subject areas
- Medicine(all)
Cite this
Supervised method for construction of microRNA-mRNA networks : application in cardiac tissue aging dataset. / Dimitrakopoulos, Georgios N.; Dimitrakopoulou, Konstantina; Maraziotis, Ioannis A.; Sgarbas, Kyriakos; Bezerianos, Anastasios.
In: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, Vol. 2014, 2014, p. 318-321.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Supervised method for construction of microRNA-mRNA networks
T2 - application in cardiac tissue aging dataset
AU - Dimitrakopoulos, Georgios N.
AU - Dimitrakopoulou, Konstantina
AU - Maraziotis, Ioannis A.
AU - Sgarbas, Kyriakos
AU - Bezerianos, Anastasios
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84942915496&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84942915496&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2014.6943593
DO - 10.1109/EMBC.2014.6943593
M3 - Article
C2 - 25569961
VL - 2014
SP - 318
EP - 321
JO - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
JF - Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
SN - 1557-170X
ER -