Author, Subjects, Keywords

Cited Author

 
 
   » By Author or Editor
 » Browse Author by Alphabet
 » By Journal
 » By Subjects
 » Malaysian Journals
 » By Type
 » By Year
 » By Latest Additions
 
 
   » By Author
 » Top 20 Authors
 » Top 20 Article
 » Top Journal Cited
 » Top Article Cited
 » Journal Citation Statistics
 » Usage Since Sept 2007


 
 
 

Login | Create Account

Time-series Microarray Cluster Analysis Towards Mining the Effects of Treatment

Loh, Wei Ping, and Yahya Abu Hasan, (2008) Time-series Microarray Cluster Analysis Towards Mining the Effects of Treatment. Malaysian Journal of Biochemistry and Molecular Biology, 16 (2). pp. 20-27. ISSN ISSN 1511-2616

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
400Kb

Official URL: http://ejum.fsktm.um.edu.my/ArticleInformation.aspx?ArticleID=648

Affiliations

Universiti Sains Malaysia, School of Mathematical Sciences
Universiti Sains Malaysia, School of Mathematical Sciences

Abstract

This paper presents clustering analysis using NOD Mice species microarray, from publicly available data at RNA Abundance Database (RAD). The analysis was supported by Short Time-Series Expression Miner (STEM) tool. The raw data involved short records of temporal gene expression data consisting nominal (gene accession numbers) and numerical (quantitative gene expression values) scales. NOD Mice species revealed a susceptibility to development of autoimmune insulin dependent diabetes mellitus (IDDM) obtained from pancreatic islets which are experimentally analyzed using Affymetrix microarray platform for treating the Type 1 diabetes. Type 1 diabetes refers to autoimmune diseases where immune system mistakenly attacks itself and destroys its own insulin-producing cells. The immune system attacks the healthy beta cells which produce insulin in pancreas and consequently unable to balance the body sugar. From this data, we attempted to study correlations and variations effects among samples of similar time steps. Simple statistical analysis and hypotheses testing on data replications were also analyzed to select relevant features of data and eliminate redundancies of expressions prior to clustering study. The major goal in this study was to investigate the efficiencies of Cyclophosphamide treatment imposed on Type 1 diabetes based on clustering analysis.

Item Type:Journal
Keywords:Correlation, Gene Expression, Microarray, Temporal, Time-point
Subjects:Q Science, Computer Science
ID Code:5273

1. Krebs O, Golebiewski M, Kania R, Mir S, Saric J, Weidemann A, Wittig U, Rojas I. SABIO-RK: A data warehouse for biochemical reactions and their kinetics. J Integrative Bioinformatics 2007; 4: 49.

2. Strauch J, Supper J, Spieth C, Wanke D, Kilian J, Harter K, Zell A. A two-step clustering for 3-D gene expression data reveals the main features of the Arabidopsis stress response. J Integrative Bioinformatics 2007; 4:54.

3. Zhu H, Tang Y, Ivanciu L, Centola M, Lupu C, Taylor FB Jr, Lupu F. Temporal dynamics of gene expression in the lung in a baboon model of E. Coli sepsis. BMC Bioinformatics 2007; 8: 58.

4. Joshi A, Van de Peer Y, Michoel T. Analysis of a Gibbs sampler method for model-based clustering of gene expression data. Bioinformatics 2008; 24:176-183.

5. RAD (RNA Abundance Database). Available at: www.cbil.upenn.edu/RAD/php/. Accessed July 6, 2006.

6. Yang YH, Buckley MJ, Dudoit S, Speed TP. Comparison of methods for image analysis on cDNA microarray data. J Comput Biol 2002; 9: 55-66.

7. Draghici S. Data analysis tools for DNA microarrays. Washington, DC: Chapman & Hall/CRC 2003.

8. Ernst J, Nau GJ, Bar-Joseph Z. Clustering short timeseries expression data. Bioinformatics 2005; 21: i159-i168.

9. Pidwirny M. Inferential statistics: regression and correlation. Fundamentals of Physical Geography 2nd edition. Canada, 2006.

Repository Staff Only: item control page
 
 
 
 
 
 
© Copyright 2007 MyAIS Faculty of Computer Science and Information Technology, University of Malaya – All Rights Reserved. Malaysian Abstracting and Indexing System is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.