Using cluster analysis to improve gene selection in the formation of discriminant rules for the prediction of disease outcomes

Ng, Shu-Kay and McLachlan, Geoffrey J. (2013). Using cluster analysis to improve gene selection in the formation of discriminant rules for the prediction of disease outcomes. In: Bioinformatics and Biomedicine (BIBM), 2013 IEEE International Conference on. IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013, Shanghai, China, (267-272). 18 - 21 December 2013. doi:10.1109/BIBM.2013.6732501


Author Ng, Shu-Kay
McLachlan, Geoffrey J.
Title of paper Using cluster analysis to improve gene selection in the formation of discriminant rules for the prediction of disease outcomes
Conference name IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
Conference location Shanghai, China
Conference dates 18 - 21 December 2013
Proceedings title Bioinformatics and Biomedicine (BIBM), 2013 IEEE International Conference on
Journal name Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
Place of Publication Piscataway, NJ United States
Publisher I E E E
Publication Year 2013
Year available 2013
Sub-type Fully published paper
DOI 10.1109/BIBM.2013.6732501
Open Access Status
ISBN 9781479913091
Start page 267
End page 272
Total pages 6
Collection year 2014
Language eng
Abstract/Summary The identification of genes that have different expression levels in a known number of distinct disease phenotypes contributes significantly to the construction of a discriminant rule (classifier) for predicting the class of origin of an unclassified tissue sample. Existing methods for detecting differentially-expressed genes are mainly based on multiple hypothesis tests. Clustering-based approaches either work on gene-specific summary statistics or reduced forms of gene-expression profiles. Advancement in clustering-based approaches that work on full profiling data has been minor, due to the methodological barriers for assessing differential expression between tissue classes from identified clusters of genes. In this paper, we adopt a clustering-based approach, which works on full gene-expression profiles and draws inference on differential expression using weighted contrasts of mixed effects. With a real published gene-expression data set, we show that the proposed clustering-based approach can provide a list of marker genes that improves the prediction of disease outcomes. Comparisons with existing methods are also provided using simulated data.
Subjects 2204 Religion and Religious Studies
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Conference Paper
Collections: School of Mathematics and Physics
Official 2014 Collection
 
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