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OJB®
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
ABSTRACT
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