Classification of Alzheimer Diagnosis from ADNI Plasma Biomarker Data

Authors:
Jue Mo;Sana Siddiqui;Stuart Maudsley;Huey Cheung;Bronwen Martin;Calvin A. Johnson
Affiliations:
Division of Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, MD 20892, USA;Receptor Pharmacology Unit, National Institute on Aging, National Institutes of Health Baltimore, MD 21224, USA;Receptor Pharmacology Unit, National Institute on Aging, National Institutes of Health Baltimore, MD 21224, USA;Div. of Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, MD 20892, USA;Metabolism Unit, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA;Division of Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, MD 20892, USA
Venue:
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Year:
2013

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Abstract

Research into modeling the progression of Alzheimer's disease (AD) has made recent progress in identifying plasma proteomic biomarkers to identify the disease at the pre-clinical stage. In contrast with cerebral spinal fluid (CSF) biomarkers and PET imaging, plasma biomarker diagnoses have the advantage of being cost-effective and minimally invasive, thereby improving our understanding of AD and hopefully leading to early interventions as research into this subject advances. The Alzheimer's Disease Neuroimaging Initiative* (ADNI) has collected data on 190 plasma analytes from individuals diagnosed with AD as well subjects with mild cognitive impairment and cognitively normal (CN) controls. We propose an approach to classify subjects as AD or CN via an ensemble of classifiers trained and validated on ADNI data. Classifier performance is enhanced by an augmentation of a selective biomarker feature space with principal components obtained from the entire set of biomarkers. This procedure yields accuracy of 89% and area under the ROC curve of 94%.