A Metaheuristic Approach for Simultaneous Gene Selection and Clustering of Microarray Data

2015 ◽  
pp. 449-461 ◽  
Author(s):  
P. S. Deepthi ◽  
Sabu M. Thampi
Keyword(s):  
Microarray Data ◽  
Gene Selection

A Review of Gene Selection Tools in Classifying Cancer Microarray Data

2017 ◽  
Vol 12 (3) ◽  
pp. 202-212 ◽  
Author(s):  
Tham W. Shi ◽  
Wong S. Kah ◽  
Mohd S. Mohamad ◽  
Kohbalan Moorthy ◽  
Safaai Deris ◽  
...  
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Selection Tools

Gene Selection and Classification of Human Lymphoma from Microarray Data

2005 ◽  
pp. 379-390
Author(s):  
Joarder Kamruzzaman ◽  
Suryani Lim ◽  
Iqbal Gondal ◽  
Rezaul Begg
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Human Lymphoma

scholarly journals Random forest for gene selection and microarray data classification

2011 ◽  
Vol 7 (3) ◽  
pp. 142-146 ◽  
Author(s):  
Kohbalan Moorthy ◽  
Mohd Saberi Mohamad
Keyword(s):  
Random Forest ◽  
Microarray Data ◽  
Gene Selection ◽  
Data Classification

scholarly journals Microarray Data Analysis: From Hypotheses to Conclusions Using Gene Expression Data

2004 ◽  
Vol 26 (5-6) ◽  
pp. 279-290
Author(s):  
Nicola J. Armstrong ◽  
Mark A. van de Wiel
Keyword(s):  
Microarray Data ◽  
Biological Networks ◽  
Multiple Testing ◽  
Gene Selection ◽  
Statistical Testing ◽  
Microarray Data Analysis ◽  
Gene Sets ◽  
New Development ◽  
Statistical Issues ◽  
Critical Interpretation

We review several commonly used methods for the design and analysis of microarray data. To begin with, some experimental design issues are addressed. Several approaches for pre‐processing the data (filtering and normalization) before the statistical analysis stage are then discussed. A common first step in this type of analysis is gene selection based on statistical testing. Two approaches, permutation and model‐based methods are explained and we emphasize the need to correct for multiple testing. Moreover, powerful approaches based on gene sets are mentioned. Clustering of either genes or samples is frequently performed when analyzing microarray data. We summarize the basics of both supervised and unsupervised clustering (classification). The latter may be of use for creating diagnostic arrays, for example. Construction of biological networks, such as pathways, is a statistically challenging but complex task that is a relatively new development and hence mentioned only briefly. We finish with some remarks on literature and software. The emphasis in this paper is on the philosophy behind several statistical issues and on a critical interpretation of microarray related analysis methods.

A Granular Self-Organizing Map for Clustering and Gene Selection in Microarray Data

2016 ◽  
Vol 27 (9) ◽  
pp. 1890-1906 ◽  
Author(s):  
Shubhra Sankar Ray ◽  
Avatharam Ganivada ◽  
Sankar K. Pal
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Self Organizing Map ◽  
Self Organizing
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