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A Statistical and Biological Approach for identifying misdiagnosis of incipient Alzheimer patients Using Gene expression Data

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3 Author(s)
Sandeep Joseph ; Rhodes Centre for Animal and Dairy Science, University of Georgia, Athens, GA 30602, USA. e-mail: ; Kelly R. Robbins ; Romdhane Rekaya

A latent-threshold model and misclassification algorithm were implemented to examine potential misdiagnosis among 16 Alzheimer's disease (AD) subjects using gene expression data. Results obtained without invoking the misclassification algorithm showed limited predictive power of the model. When the misclassification algorithm was invoked, four subjects were identified as being potentially misdiagnosed. Results obtained after adjustment of the AD status of these four samples showed a significant increase in the model's predictive ability. Mixed model analysis detected no AD related genes as differentially expressed when using original classifications; conversely, multiple AD genes were identified using the new classifications. These results suggest that this algorithm can identify misclassified subjects which, in turn, can increase power to predict disease status and identify disease related genes

Published in:

Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE

Date of Conference:

Aug. 30 2006-Sept. 3 2006