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Novel Approaches for Feature Extraction and Representation Learning of Alzheimer's Biomarkers
Abstract
Alzheimer's disease (AD), a severe and complex neurodegeneration disease, are relatively invasive and expensive. They depend on measures of amyloid-β1-42 (Aβ42), general tau protein, and hyperphosphorylated tau (p-tau) in cerebrospinal fluid (CSF), in addition to cognitive tests and imaging methods. The biomarkers linked to Alzheimer's disease (AD) are essential for the early diagnosis and therapeutic intervention, the recognition and examination. This work leverages the latest developments in machine learning and data analytics to provide unique methods for feature extraction and representation learning of Alzheimer's biomarkers. Alzheimer's disease is a complicated neurological condition for which there are currently no reliable diagnostic techniques. The methods used now rely on imaging, cognitive assessments, and biomarkers found in the cerebrospinal fluid (CSF), such as amyloid-β1-42 (Aβ42), phosphorylated tau (p-tau), and total tau. These methods are expensive and intrusive, and they have limitations with regard to sensitivity and specificity.
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