@inbook{b485dc4405754e3b86a6fd9375ab9a7c,
title = "Co-expression Networks in Predicting Transcriptional Gene Regulation",
abstract = "Recent progress in transcriptomics and co-expression networks have enabled us to predict the inference of the biological functions of genes with the associated environmental stress. Microarrays and RNA sequencing (RNA-seq) are the most commonly used high-throughput gene expression platforms for detecting differentially expressed genes between two (or more) phenotypes. Gene co-expression networks (GCNs) are a systems biology method for capturing transcriptional patterns and predicting gene interactions into functional and regulatory relationships. Here, we describe the procedures and tools used to construct and analyze GCN and investigate the integration of transcriptional data with GCN to provide reliable information about the underlying biological mechanism.",
keywords = "Biological networks, Co-expression networks, Network analysis, Systems biology, Target gene identification, Transcriptomics",
author = "AbuQamar, {Synan F.} and El-Tarabily, {Khaled A.} and Arjun Sham",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Science+Business Media, LLC, part of Springer Nature.",
year = "2021",
doi = "10.1007/978-1-0716-1534-8_1",
language = "English",
series = "Methods in Molecular Biology",
publisher = "Humana Press Inc.",
pages = "1--11",
booktitle = "Methods in Molecular Biology",
}