Farhadian, M, Lisboa, P, Moghimbeigi, A, Poorolajal, J and Mahjub, H (2014) Supervised wavelet method to predict patient survival from gene expression data. Scientific World Journal, 2014. ISSN 1537-744X

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Abstract

In microarray studies, the number of samples is relatively small compared to the number of genes per sample. An important aspect of microarray studies is the prediction of patient survival based on their gene expression profile. This naturally calls for the use of a dimension reduction procedure together with the survival prediction model. In this study, a new method based on combining wavelet approximation coefficients and Cox regression was presented. The proposed method was compared with supervised principal component and supervised partial least squares methods. The different fitted Cox models based on supervised wavelet approximation coefficients, the top number of supervised principal components, and partial least squares components were applied to the data. The results showed that the prediction performance of the Cox model based on supervised wavelet feature extraction was superior to the supervised principal components and partial least squares components. The results suggested the possibility of developing new tools based on wavelets for the dimensionally reduction of microarray data sets in the context of survival analysis.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology
Divisions:	Applied Mathematics (merged with Comp Sci 10 Aug 20)
Publisher:	Hindawi Publishing Corporation
Related URLs:	http://www.ncbi.nlm.nih.gov/pubmed/25538955
Date Deposited:	04 Nov 2015 13:36
Last Modified:	04 Sep 2021 04:28
DOI or ID number:	10.1155/2014/618412
URI:	https://researchonline.ljmu.ac.uk/id/eprint/2282

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