Topological and biological assessment of gene networks using miRNA- target gene data

In recent years, different biological data sets obtained by the next generation sequencing techniques have enhanced the analysis of the underlying molecular interactions of diseases. In our study we apply ARNetMiT, C3NET, WGCNA and ARACNE algorithms on microRNA-target gene datasets to infer gene coe...

Full description

Saved in:
Bibliographic Details
Published in:2019 Innovations in Intelligent Systems and Applications Conference (ASYU) pp. 1 - 4
Main Authors: Cingiz, Mustafa Ozgur, Diri, Banu
Format: Conference Proceeding
Language:English
Turkish
Published: IEEE 01.10.2019
Subjects:
Bioinformatics
Breast
Cancer
gene coexpression networks
gene network inference algorithms
gene ontology terms
miRNA-target genes
Nanoelectromechanical systems
Proteins
RNA
scale free networks
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In recent years, different biological data sets obtained by the next generation sequencing techniques have enhanced the analysis of the underlying molecular interactions of diseases. In our study we apply ARNetMiT, C3NET, WGCNA and ARACNE algorithms on microRNA-target gene datasets to infer gene coexpression networks of breast, prostate, colon and pancreatic cancers. Gene coexpression networks are evaluated according to their topological and biological features. WGCNA based gene coexpression networks fits to scale free network topology more than other gene coexpression networks. In biological assessment there is no obvious difference found between gene coexpression networks which derived from different algorithms.
DOI:10.1109/ASYU48272.2019.8946426