title: A diagnostic methodology for Alzheimer's disease.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Systems Biology Lab, University of Florida, Gainesville, FL, 32611, USA. wenchin@ufl.edu.
authors.forename: Wen-Chin
authors.initials: WC
authors.lastname: Hsu
authors.forename: Christopher
authors.initials: C
authors.lastname: Denq
authors.forename: Su-Shing
authors.initials: SS
authors.lastname: Chen