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Online Journal of Bioinformatics ©

 Volume 7 (1): 22-31, 2006.

Classification of incipient Alzheimer patients using gene expression data: Dealing with potential misdiagnosis.

Robbins K1, Joseph S1, Zhang W1, Rekaya R1,2,  Bertrand JK1

1Department of Animal and Dairy Science, 2Department of Statistics University of Georgia, Athens, Georgia USA.




Robbins K, Joseph S, Zhang W, Rekaya R,  Bertrand JK, Classification of incipient Alzheimer patients using gene expression data: Dealing with potential misdiagnosis, Onl J Bioinform., 7 (1) : 22-31, 2006. A latent-threshold model and misclassification algorithm were implemented to predict the Alzheimer’s disease (AD) status of 16 subjects using gene expression data. Each of the 16 subjects was initially classified as healthy or incipient AD using clinical tests. To examine possible age effects on the diagnosis of incipient AD, two datasets were created containing the age unadjusted (D1) and age adjusted (D2) expression of the 100 most informative genes. Control and incipient subjects were clustered into old and young age classes which were then used for age adjustments. Results obtained without invoking the misclassification algorithm showed limited predictive power of the model using either D1 or D2. When the misclassification algorithm was invoked, four subjects were identified as being potentially misdiagnosed. Results obtained after adjustment of the AD status (switching of the binary status) of these four samples showed a significant increase in the model’s predictive ability. Further examination of the misdiagnosed samples, using plots and tests, showed that the gene expression of these samples agreed more with the new than the initial classification. Similar results were obtained using either D1 or D2. Interestingly, all of the misdiagnosed subjects were originally classified as either an old control or a young incipient. These results suggest that gene expression can be used to improve AD diagnosis by identifying potentially misdiagnosed subjects in the training set. Moreover, it was found that age may have little influence on genes highly correlated with AD status, but it could affect diagnosis based on clinical tests.

Key words: Latent-threshold model, Misclassification algorithm, Alzheimer’s disease