DocumentCode :
1905680
Title :
Finding Characteristic Biology Patterns in Cancer Microarrays
Author :
Vass, Keith ; Grindrod, Peter ; Higham, Des ; Kalna, Gabriela ; Spence, Alastair
Author_Institution :
Beatson Inst. for Cancer Res., Strathclyde Univ., Glasgow
fYear :
2006
fDate :
9-9 Nov. 2006
Firstpage :
156
Lastpage :
166
Abstract :
Genetic and environmental differences are known to affect gene expression. The natural variance of expression of many genes affects the control system in any tissue. A finite set of controls must respond to these perturbations, causing regular patterns of altered gene expression characteristic of the system. In order to examine these ideas, a simple method the summarise and test the observed patterns is presented. In this paper, a classified microarray data is used to find relationships between genes. The quantitative data from microarrays can be classified as up or down, allowing estimation of significance by Monte Carlo methods. Spectral analysis and singular value decomposition were also used to study the gene expression pattern. Successive SVD vectors can identify obvious clusters of related genes.
Keywords :
Monte Carlo methods; cancer; genetics; medical diagnostic computing; molecular biophysics; singular value decomposition; spectral analysis; Monte Carlo methods; biology patterns; cancer microarrays; gene expression; singular value decomposition; spectral analysis; tissue;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Signal Processing for Genomics, 2006. The Institution of Engineering and Technology Seminar on
Conference_Location :
Cambridge
Print_ISBN :
0-86341-716-7
Type :
conf
Filename :
4126038
Link To Document :
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