scholarly journals Analysis of Gene Expression Data Using BRB-Array Tools

2007 ◽  
Vol 3 ◽  
pp. 117693510700300 ◽  
Author(s):  
Richard Simon ◽  
Amy Lam ◽  
Ming-Chung Li ◽  
Michael Ngan ◽  
Supriya Menenzes ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Microarray ◽  
Gene Expression Data ◽  
Gene Annotation ◽  
Gene Expression Omnibus ◽  
Expression Data ◽  
Microarray Experiments ◽  
Integrated Software ◽  
Microarray Studies ◽  
To Receive

BRB-ArrayTools is an integrated software system for the comprehensive analysis of DNA microarray experiments. It was developed by professional biostatisticians experienced in the design and analysis of DNA microarray studies and incorporates methods developed by leading statistical laboratories. The software is designed for use by biomedical scientists who wish to have access to state-of-the-art statistical methods for the analysis of gene expression data and to receive training in the statistical analysis of high dimensional data. The software provides the most extensive set of tools available for predictive classifier development and complete cross-validation. It offers extensive links to genomic websites for gene annotation and analysis tools for pathway analysis. An archive of over 100 datasets of published microarray data with associated clinical data is provided and BRB-ArrayTools automatically imports data from the Gene Expression Omnibus public archive at the National Center for Biotechnology Information.

ICN: a normalization method for gene expression data considering the over-expression of informative genes

2016 ◽  
Vol 12 (10) ◽  
pp. 3057-3066 ◽  
Author(s):  
Lixin Cheng ◽  
Xuan Wang ◽  
Pak-Kan Wong ◽  
Kwan-Yeung Lee ◽  
Le Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Normalization Method ◽  
Expression Data ◽  
Over Expression ◽  
Global Increase ◽  
Microarray Studies

The global increase of gene expression has been frequently established in cancer microarray studies.

Efficient Mining Frequent Closed Discriminative Biclusters by Sample-Growth

2013 ◽  
pp. 84-103
Author(s):  
Miao Wang ◽  
Xuequn Shang ◽  
Shaohua Zhang ◽  
Zhanhuai Li
Keyword(s):  
Gene Expression ◽  
Dna Microarray ◽  
Gene Expression Data ◽  
Biological Significance ◽  
Microarray Dataset ◽  
Experimental Results ◽  
Expression Data ◽  
Biologically Relevant ◽  
Almost All ◽  
Microarray Datasets

DNA microarray technology has generated a large number of gene expression data. Biclustering is a methodology allowing for condition set and gene set points clustering simultaneously. It finds clusters of genes possessing similar characteristics together with biological conditions creating these similarities. Almost all the current biclustering algorithms find bicluster in one microarray dataset. In order to reduce the noise influence and find more biological biclusters, the authors propose the FDCluster algorithm in order to mine frequent closed discriminative bicluster in multiple microarray datasets. FDCluster uses Apriori property and several novel techniques for pruning to mine biclusters efficiently. To increase the space usage, FDCluster also utilizes several techniques to generate frequent closed bicluster without candidate maintenance in memory. The experimental results show that FDCluster is more effective than traditional methods in either single micorarray dataset or multiple microarray datasets. This paper tests the biological significance using GO to show the proposed method is able to produce biologically relevant biclusters.

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