Subspace Clustering of Microarray Data Based on Domain Transformation

2006 ◽  
pp. 14-28 ◽  
Author(s):  
Jongeun Jun ◽  
Seokkyung Chung ◽  
Dennis McLeod
Keyword(s):  
Microarray Data ◽  
Subspace Clustering ◽  
Domain Transformation

Soft Subspace Clustering for Cancer Microarray Data Analysis

2014 ◽  
pp. 131-145
Author(s):  
Natthakan Iam-On ◽  
Tossapon Boongoen
Keyword(s):  
Microarray Data ◽  
Dimensional Space ◽  
Subspace Clustering ◽  
Research Direction ◽  
Microarray Data Analysis ◽  
Consensus Clustering ◽  
Gene Expressions ◽  
Tumor Subtypes ◽  
Microarray Gene ◽  
New Research

A need has long been identified for a more effective methodology to understand, prevent, and cure cancer. Microarray technology provides a basis of achieving this goal, with cluster analysis of gene expression data leading to the discrimination of patients, identification of possible tumor subtypes, and individualized treatment. Recently, soft subspace clustering was introduced as an accurate alternative to conventional techniques. This practice has proven effective for high dimensional data, especially for microarray gene expressions. In this review, the basis of weighted dimensional space and different approaches to soft subspace clustering are described. Since most of the models are parameterized, the application of consensus clustering has been identified as a new research direction that is capable of turning the difficulty with parameter selection to an advantage of increasing diversity within an ensemble.

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.

Subspace Clustering of DNA Microarray Data

Biotechnology ◽  
2019 ◽  
pp. 210-264
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.

Expression and network analysis of Illumina microarray data

2009 ◽  
Author(s):  
Giovanni Coppola ◽  
Kellen Winden ◽  
Genevieve Konopka ◽  
Fuying Gao ◽  
Daniel Geschwind
Keyword(s):  
Network Analysis ◽  
Microarray Data

The tyrosine kinase TEK is differentially expressed in non-small cell lung adenocarcinomas.

2020 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
United States ◽  
Tyrosine Kinase ◽  
Lung Adenocarcinoma ◽  
Microarray Data ◽  
The United States ◽  
Small Cell ◽  
Small Cell Lung ◽  
Significant Differential Expression ◽  
Lung Cancers ◽  
Lung Adenocarcinomas

Non-small cell lung adenocarcinoma (NSCLC) is a leading cause of death in the United States and worldwide (1, 2). We mined published microarray data (3, 4, 5) to discover genes associated with NSCLC. We identified significant differential expression of the tyrosine kinase TEK in tumors from patients with NSCLC. TEK may be of relevance to the initiation, progression or maintenance of non-small cell lung cancers.

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