Theta, a multidimensional ratio biomarker applied to five amyloid beta peptides for investigations in familial Alzheimer's disease
Llorente Saguer, I.; Arber, C.; Oxtoby, N. P.
Show abstract
Biomarker discovery in complex diseases often requires classifying biological states using multiple, interconnected features, a task where traditional two-feature ratios are limited. We introduce theta, a mathematical model designed to extend the ratio concept for classification across any number of input features. Theta is a multivariate normative statistical model, which quantifies the deviation of a multidimensional feature vector relative to a reference. We apply theta to amyloid-beta (A{beta}) peptide profiles to classify early-onset familial Alzheimers disease mutation carriers versus controls using multiple datasets of in vitro neuronal models. Theta consistently achieved high discrimination (AUC > 0.99, precision-recall AUC > 0.96) across multi-site datasets, considerably outperforming established A{beta} biomarkers (precision-recall AUC 0.41 is the best score from the literature, in all datasets merged). This demonstrates thetas power as a versatile mathematical tool.
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