DGL global strategies in DNA microarray gene expression analysis and data mining for human blood cancers

Authors

Dongguang Li, Edith Cowan University

Document Type

Book Chapter

Publisher

Springer

Faculty

Faculty of Computing, Health and Science

School

School of Computer and Information Science

Comments

Li, D. (2008). DGL Global Strategies in DNA Microarray Gene Expression Analysis and Data Mining for Human Blood Cancers. In S.Li (Ed.), Mouse Models of Human Blood Cancers (pp. 259-281). Springer, New York. https://doi.org/10.1007/978-0-387-69132-9_11

https://link.springer.com/chapter/10.1007/978-0-387-69132-9_11

Abstract

Computation is required to extract meaningful information from the large amount of data generated by gene expression profiling [1, 2, 3]. Most of the algorithms commonly applied to microarray data analysis have been correlation-based approaches named cluster analysis [4]. For example, an efficient two-way clustering algorithm was applied to a colon cancer data set consisting of the expression patterns of different cell types. Gene expression in 40 tumour and 22 normal colon tissue samples was analysed across 2000 genes [4]. Cluster analysis groups the genes involved in microarray data. Those clustered genes are likely to be functionally linked and need to be looked into closely. Although cluster analysis has widely been accepted in analysing the patterns of gene expression, the methods developed may not be able to fully extract the information from the microarray data corrupted by high-dimensional noise. If the noise from the genes that are irrelevant is not sufficiently...

DOI

10.1007/978-0-387-69132-9_11