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Open Access Open Badges Methodology

A diagnostic methodology for Alzheimer’s disease

Wen-Chin Hsu12*, Christopher Denq13 and Su-Shing Chen14

Author Affiliations

1 Systems Biology Lab, University of Florida, Gainesville, FL, 32611, USA

2 Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA

3 Van Nuys Senior High School, Van Nuys, CA, 91411, USA

4 Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, 32611, USA

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Journal of Clinical Bioinformatics 2013, 3:9  doi:10.1186/2043-9113-3-9

Published: 25 April 2013



Like all other neurodegenerative diseases, Alzheimer’s disease (AD) remains a very challenging and difficult problem for diagnosis and therapy. For many years, only historical, behavioral and psychiatric measures have been available to AD cases. Recently, a definitive diagnostic framework, using biomarkers and imaging, has been proposed. In this paper, we propose a promising diagnostic methodology for the framework.


In a previous paper, we developed an efficient SVM (Support Vector Machine) based method, which we have now applied to discover important biomarkers and target networks which provide strategies for AD therapy.


The methodology selects a number of blood-based biomarkers (fewer than 10% of initial numbers on three AD datasets from NCBI), and the results are statistically verified by cross-validation. The resulting SVM is a classifier of AD vs. normal subjects. We construct target networks of AD based on MI (mutual information). In addition, a hierarchical clustering is applied on the initial data and clustered genes are visualized in a heatmap. The proposed method also performs gender analysis by classifying subjects based on gender.


Unlike other traditional statistical analyses, our method uses a machine learning-based algorithm. Our method selects a small set of important biomarkers for AD, differentiates noisy (irrelevant) from relevant biomarkers and also provides the target networks of the selected biomarkers, which will be useful for diagnosis and therapeutic design. Finally, based on the gender analysis, we observe that gender could play a role in AD diagnosis.

Feature selection; Biomarkers; Target networks; Alzheimer’s disease; Support vector machine