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.

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.

Gene Expression Analysis based on Ant Colony Optimisation Classification

2016 ◽  
Vol 3 (3) ◽  
pp. 51-59 ◽  
Author(s):  
Gerald Schaefer
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Classification Performance ◽  
Ant Colony ◽  
Rule Base ◽  
Expression Data ◽  
Ant Colony Optimisation ◽  
Microarray Studies

Microarray studies and gene expression analysis have received significant attention over the last few years and provide many promising avenues towards the understanding of fundamental questions in biology and medicine. In this paper, the authors investigate the application of ant colony optimisation (ACO) based classification for the analysis of gene expression data. They employ cAnt-Miner, a variation of the classical Ant-Miner classifier, which is capable of interpreting the numerical gene expression data. Experimental results on well-known gene expression datasets show that the ant-based approach is capable of extracting a compact rule base while providing good classification performance.

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