Artificial Immune Recognition System for DNA Microarray Data Analysis

2008 ◽  
Author(s):  
Chuanliang Chen ◽  
Chuan Xu ◽  
Rongfang Bie ◽  
X. Z. Gao
Keyword(s):  
Data Analysis ◽  
Dna Microarray ◽  
Microarray Data ◽  
Recognition System ◽  
Microarray Data Analysis ◽  
Immune Recognition ◽  
Artificial Immune ◽  
Artificial Immune Recognition System ◽  
Dna Microarray Data

Biclustering of DNA Microarray Data

2013 ◽  
pp. 513-551 ◽  
Author(s):  
Alain B. Tchagang ◽  
Youlian Pan ◽  
Fazel Famili ◽  
Ahmed H. Tewfik ◽  
Panayiotis V. Benos
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Dna Microarray ◽  
High Throughput ◽  
Microarray Data ◽  
Evaluation Methods ◽  
Microarray Data Analysis ◽  
Dna Microarray Data ◽  
Daunting Task

In this chapter, different methods and applications of biclustering algorithms to DNA microarray data analysis that have been developed in recent years are discussed and compared. Identification of biological significant clusters of genes from microarray experimental data is a very daunting task that emerged, especially with the development of high throughput technologies. Various computational and evaluation methods based on diverse principles were introduced to identify new similarities among genes. Mathematical aspects of the models are highlighted, and applications to solve biological problems are discussed.

Identification of target genes conferring ethanol stress tolerance to Saccharomyces cerevisiae based on DNA microarray data analysis

2007 ◽  
Vol 131 (1) ◽  
pp. 34-44 ◽  
Author(s):  
Takashi Hirasawa ◽  
Katsunori Yoshikawa ◽  
Yuki Nakakura ◽  
Keisuke Nagahisa ◽  
Chikara Furusawa ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Data Analysis ◽  
Stress Tolerance ◽  
Dna Microarray ◽  
Microarray Data ◽  
Target Genes ◽  
Microarray Data Analysis ◽  
Ethanol Stress ◽  
Dna Microarray Data

A composite gene selection for DNA microarray data analysis

2013 ◽  
Vol 74 (20) ◽  
pp. 9031-9041 ◽  
Author(s):  
Dong Kyun Park ◽  
Eun-Young Jung ◽  
Sang-Hong Lee ◽  
Joon S. Lim
Keyword(s):  
Data Analysis ◽  
Dna Microarray ◽  
Microarray Data ◽  
Gene Selection ◽  
Microarray Data Analysis ◽  
Dna Microarray Data ◽  
Selection For

Subspace Clustering of DNA Microarray Data

2014 ◽  
Vol 4 (2) ◽  
pp. 1-52
Author(s):  
Alain B. Tchagang ◽  
Fazel Famili ◽  
Youlian Pan
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Dna Microarray ◽  
High Throughput ◽  
Microarray Data ◽  
Subspace Clustering ◽  
Microarray Data Analysis ◽  
Dna Microarray Data ◽  
Daunting Task ◽  
Insight Into

Identification of biological significant subspace clusters (biclusters and triclusters) of genes from microarray experimental data is a very daunting task that emerged, especially with the development of high throughput technologies. Several methods and applications of subspace clustering (biclustering and triclustering) in DNA microarray data analysis have been developed in recent years. Various computational and evaluation methods based on diverse principles were introduced to identify new similarities among genes. This review discusses and compares these methods, highlights their mathematical principles, and provides insight into the applications to solve biological problems.

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