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.

scholarly journals CIDA: An integrated software for the design, characterisation and global comparison of microarrays

2007 ◽  
Vol 4 (3) ◽  
pp. 224-242
Author(s):  
Sabah Khalid ◽  
Mohsin Khan ◽  
Alistair Symonds ◽  
Karl Fraser ◽  
Ping Wang ◽  
...  
Keyword(s):  
Human Genome ◽  
Microarray Data ◽  
Expression Profiles ◽  
Human Life ◽  
Microarray Data Analysis ◽  
Microarray Technology ◽  
Gene Chips ◽  
Global Comparison ◽  
Effective Manner ◽  
Microarray Gene

Abstract Microarray technology has had a significant impact in the field of systems biology involving the investigation into the biological systems that regulate human life. Identifying genes of significant interest within any given disease on an individual basis is no doubt time consuming and inefficient when considering the complexity of the human genome. Thus, the genetic profiling of the entire human genome in a single experiment has resulted in microarray technology becoming a widely used experimental tool. However, without the use of tools for several aspects of microarray data analysis the technology is limited. To date, no such tool has been developed that allows the integration of numerous microarray results from different research laboratories as well as the design of customised gene chips in a cost-effective manner. In light of this, we have designed the first integrated and automated software called Chip Integration, Design and Annotation (CIDA) for the cross comparison, design and functional annotation of microarray gene chips. The software provides molecular biologists with the control to cross compare the biological signatures generated from multiple microarray studies, design custom microarray gene chips based on their research requirements and lastly characterise microarray data in the context of immunogenomics. Through the relative comparison of related microarray experiments we have identified 258 genes with common gene expression profiles that are not only upregulated in anergic T cells, but also in cells over-expressing the transcription factor Egr2, that has been identified to play a role in T cell anergy. Using the gene chip design aspect of CIDA we have designed and subsequently fabricate immuno-tolerance gene chips consisting of 1758 genes for further research.The software and database schema is freely available at ftp://ftp.brunel.ac.uk/cspgssk/CIDA/. Additional material is available online at http://www.brunel.ac.uk/about/acad/health/healthres/researchgroups/mi/publications/supplementary/cida

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.

A Robust Blind Image Forensic-Detection Method based on Fuzzy Technique

2019 ◽  
Vol 8 (3) ◽  
pp. 5926-5929
Keyword(s):  
Research Direction ◽  
Geometric Transformation ◽  
Detection Accuracy ◽  
Forensic Investigation ◽  
Image Forensic ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Tampered Image ◽  
New Research ◽  
Blind Image

Blind forensic-investigation in a digital image is a new research direction in image security. It aims to discover the altered image content without any embedded security scheme. Block and key point based methods are the two dispensation options in blind image forensic investigation. Both the techniques exhibit the best performance to reveal the tampered image. The success of these methods is limited due to computational complexity and detection accuracy against various image distortions and geometric transformation operations. This article introduces different blind image tampering methods and introduces a robust image forensic investigation method to determine the copy-move tampered image by means of fuzzy logic approach. Empirical outcomes facilitate that the projected scheme effectively classifies copy-move type of forensic images as well as blurred tampered image. Overall detection accuracy of this method is high over the existing methods.

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