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Bioprofile analysis: a new approach for the analysis of biomedical data in Alzheimer's disease

Research output: Contribution to journalArticle



Javier Escudero, Emmanuel Ifeachor, John P Zajicek, Alzheimer’s Disease Neuroimaging Initiative

School/Research organisations


This article presents a new approach for the analysis of biomedical data to support the management and care of patients with Alzheimer's disease (AD). The increase in prevalence of neurodegenerative disorders such as AD has led to a need for objective means to assist clinicians with the analysis and interpretation of complex biomedical data. To this end, we propose a "Bioprofile" analysis to reveal the pattern of disease in the subject's biodata. From the Bioprofile, personal "Bioindices" that indicate how closely a subject's data resemble the pattern of AD can be derived. We used an unsupervised technique (k-means) to cluster variables of the ADNI database so that subjects are divisible into those with the Bioprofile of AD and those without it. Results revealed that there is an "AD pattern" in the biodata of most AD and mild cognitive impairment (MCI) patients and some controls. This pattern agrees with a recent hypothetical model of AD evolution. We also assessed how the Bioindices changed with time and we found that the Bioprofile was associated with the risk of progressing from MCI to AD. Hence, the Bioprofile analysis is a promising methodology that may potentially provide a complementary new way of interpreting biomedical data. Furthermore, the concept of the Bioprofile could be extended to other neurodegenerative diseases.



Original languageEnglish
Pages (from-to)997-1010
Number of pages14
JournalJournal of Alzheimer's Disease
Issue number4
Publication statusPublished - 2012

    Research areas

  • Alzheimer Disease, Cluster Analysis, Databases, Factual, Diagnostic Imaging, Disease Progression, Humans, Neuropsychological Tests, Statistics as Topic

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