Abstract: Computer-implemented methods and systems are provided for the analysis of multiplex fluorescent-dyed microsphere assays. The methods of the invention provide for determination of differences in analyte quantities between samples obtained from multiplex fluorescent-dyed microsphere assays by analysis of individual bead fluorescence and adjusting for variance; variance-stabilization of the data, and determining significance with hypothesis testing with tolerance determined by power estimation. The methods of the invention provide a benefit in allowing access to low signal or poor quality data, increased statistical power and decreased variability compared to standard curve methodology.

Abstract: The present invention identifies circulating proteins that are differentially expressed in atherosclerosis. Circulating levels of these proteins, particularly as a panel of proteins, can discriminate patients with acute myocardial infarction from those with stable exertional angina and from those with no history of atherosclerotic cardiovascular disease. Such levels can also predict cardiovascular events, determine the effectiveness of therapy, stage disease, and the like. For example, these markers are useful as surrogate biomarkers of clinical events needed for development of vascular specific pharmaceutical agents.

Abstract: The present invention provides a powerful and robust classification and prediction tool, methodology, and architecture for supervised learning, particularly applicable to complex datasets where multiple factors determine an outcome and yet many other factors are irrelevant to prediction. Among those features which are relevant to the outcome, they have complicated and influential interactions, though insignificant individual contributions. For example, polygenic diseases may be associated with genetic and environmental risk factors. This new approach allow us consider all risk factors simultaneously, including interactions and combined effects. Our approach has the strength of both binary classification trees and regression. A simple rooted binary tree model is created with each split defined by a linear combination of selected variables. The linear combination is achieved by regression with optimal scoring. The variables are selected using backward shaving.

Abstract: The present invention provides a powerful and robust classification and prediction tool, methodology, and architecture for supervised learning, particularly applicable to complex datasets where multiple factors determine an outcome and yet many other factors are irrelevant to prediction. Among those features which are relevant to the outcome, they have complicated and influential interactions, though insignificant individual contributions. For example, polygenic diseases may be associated with genetic and environmental risk factors. This new approach allow us consider all risk factors simultaneously, including interactions and combined effects. Our approach has the strength of both binary classification trees and regression. A simple rooted binary tree model is created with each split defined by a linear combination of selected variables. The linear combination is achieved by regression with optimal scoring. The variables are selected using backward shaving.