The main goal of Systems Biology research is to reconstruct biological networks for its topological analysis so that reconstructed networks can be used for the identification of various kinds of disease. The availability of high-throughput data generated by microarray experiments fuelled researchers to use whole-genome gene expression profiles to understand cancer and to reconstruct key cancer-specific gene
regulatory network. Now, the researchers are taking a keen interest in the development of algorithm for the reconstruction of gene regulatory network from whole genome expression profiles. In this study, a cancer-specific gene regulatory network (prostate cancer) has been constructed using a simple and novel statistics based approach. First, significant genes differentially expressing them self in the disease
condition has been identified using a two-stage filtering approach t-test and fold-change measure. Next, regulatory relationships between the identified genes has been computed using Pearson correlation coefficient. The obtained results has been validated with the available databases and literatures. We obtained a cancer-specific regulatory network of 29 genes with a total of 55 regulatory relations in which
some of the genes has been identified as hub genes that can act as drug target for the cancer diagnosis
%0 Journal Article
%1 noauthororeditor
%A Raza, Khalid
%A Jaiswal, Rajni
%D 2013
%J International Journal on Bioinformatics & Biosciences (IJBB)
%K Gene analysis cancer differentially expressed genes microarray network prostate regulatory
%N Number 2
%P 27-36
%R 10.5121/ijbb.2013.3203
%T RECONSTRUCTION AND ANALYSIS OF CANCERSPECIFIC GENE REGULATORY NETWORKS FROM GENE EXPRESSION PROFILES
%U https://wireilla.com/papers/ijbb/V3N2/3213ijbb03.pdf
%V Volume 3
%X The main goal of Systems Biology research is to reconstruct biological networks for its topological analysis so that reconstructed networks can be used for the identification of various kinds of disease. The availability of high-throughput data generated by microarray experiments fuelled researchers to use whole-genome gene expression profiles to understand cancer and to reconstruct key cancer-specific gene
regulatory network. Now, the researchers are taking a keen interest in the development of algorithm for the reconstruction of gene regulatory network from whole genome expression profiles. In this study, a cancer-specific gene regulatory network (prostate cancer) has been constructed using a simple and novel statistics based approach. First, significant genes differentially expressing them self in the disease
condition has been identified using a two-stage filtering approach t-test and fold-change measure. Next, regulatory relationships between the identified genes has been computed using Pearson correlation coefficient. The obtained results has been validated with the available databases and literatures. We obtained a cancer-specific regulatory network of 29 genes with a total of 55 regulatory relations in which
some of the genes has been identified as hub genes that can act as drug target for the cancer diagnosis
@article{noauthororeditor,
abstract = {The main goal of Systems Biology research is to reconstruct biological networks for its topological analysis so that reconstructed networks can be used for the identification of various kinds of disease. The availability of high-throughput data generated by microarray experiments fuelled researchers to use whole-genome gene expression profiles to understand cancer and to reconstruct key cancer-specific gene
regulatory network. Now, the researchers are taking a keen interest in the development of algorithm for the reconstruction of gene regulatory network from whole genome expression profiles. In this study, a cancer-specific gene regulatory network (prostate cancer) has been constructed using a simple and novel statistics based approach. First, significant genes differentially expressing them self in the disease
condition has been identified using a two-stage filtering approach t-test and fold-change measure. Next, regulatory relationships between the identified genes has been computed using Pearson correlation coefficient. The obtained results has been validated with the available databases and literatures. We obtained a cancer-specific regulatory network of 29 genes with a total of 55 regulatory relations in which
some of the genes has been identified as hub genes that can act as drug target for the cancer diagnosis},
added-at = {2018-08-27T09:13:29.000+0200},
author = {Raza, Khalid and Jaiswal, Rajni},
biburl = {https://www.bibsonomy.org/bibtex/2b7f47b8de6638275cf9eacb9bb836545/jack-12},
doi = {10.5121/ijbb.2013.3203},
interhash = {464556bd044b4c7eec9bea6b7c865750},
intrahash = {b7f47b8de6638275cf9eacb9bb836545},
journal = {International Journal on Bioinformatics & Biosciences (IJBB)},
keywords = {Gene analysis cancer differentially expressed genes microarray network prostate regulatory},
language = {english},
month = {June},
number = { Number 2},
pages = {27-36},
timestamp = {2018-08-27T09:13:29.000+0200},
title = {RECONSTRUCTION AND ANALYSIS OF CANCERSPECIFIC GENE REGULATORY NETWORKS FROM GENE EXPRESSION PROFILES},
url = {https://wireilla.com/papers/ijbb/V3N2/3213ijbb03.pdf},
volume = {Volume 3},
year = 2013
}