4 citations · 16 across the 11 of their papers we have counts for
12 papers
Analysis of Diagnostics (Part I): Prevalence, Uncertainty Quantification, and Machine Learning
Paul N. Patrone, Raquel A. Binder, Catherine S. Forconi +2
Diagnostic testing provides a unique setting for studying and developing tools in classification theory. In such contexts, the concept of prevalence, i.e. the number of individuals…
Optimal classification and generalized prevalence estimates for diagnostic settings with more than two classes
Rayanne A. Luke, Anthony J. Kearsley, Paul N. Patrone
An accurate multiclass classification strategy is crucial to interpreting antibody tests. However, traditional methods based on confidence intervals or receiver operating character…
Prevalence Estimation and Optimal Classification Methods to Account for Time Dependence in Antibody Levels
Prajakta Bedekar, Anthony J. Kearsley, Paul N. Patrone
Serology testing can identify past infection by quantifying the immune response of an infected individual providing important public health guidance. Individual immune responses ar…
Reproducibility in Cytometry: Signals Analysis and its Connection to Uncertainty Quantification
Paul N. Patrone, Matthew DiSalvo, Anthony J. Kearsley +2
Signals analysis for cytometry remains a challenging task that has a significant impact on uncertainty. Conventional cytometers assume that individual measurements are well charact…
Modeling in higher dimensions to improve diagnostic testing accuracy: theory and examples for multiplex saliva-based SARS-CoV-2 antibody assays
Rayanne A. Luke, Anthony J. Kearsley, Nora Pisanic +4
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has emphasized the importance and challenges of correctly interpreting antibody test results. Identificati…
Minimizing Uncertainty in Prevalence Estimates
Paul Patrone, Anthony Kearsley
Estimating prevalence, the fraction of a population with a certain medical condition, is fundamental to epidemiology. Traditional methods rely on classification of test samples tak…
Optimal Decision Theory for Diagnostic Testing: Minimizing Indeterminate Classes with Applications to Saliva-Based SARS-CoV-2 Antibody Assays
Paul N. Patrone, Prajakta Bedekar, Nora Pisanic +4
In diagnostic testing, establishing an indeterminate class is an effective way to identify samples that cannot be accurately classified. However, such approaches also make testing…
Predicting Kovats Retention Indices Using Graph Neural Networks
Chen Qu, Barry I. Schneider, Anthony J. Kearsley +2
The \kovats retention index is a dimensionless quantity that characterizes the rate at which a compound is processed through a gas chromatography column. This quantity is independe…
Classification Under Uncertainty: Data Analysis for Diagnostic Antibody Testing
Paul N. Patrone, Anthony J. Kearsley
Formulating accurate and robust classification strategies is a key challenge of developing diagnostic and antibody tests. Methods that do not explicitly account for disease prevale…
Improving Baseline Subtraction for Increased Sensitivity of Quantitative PCR Measurements
Paul N. Patrone, Anthony J. Kearsley, Erica L. Romsos +1
Motivated by the current COVID-19 health-crisis, we examine the task of baseline subtraction for quantitative polymerase chain-reaction (qPCR) measurements. In particular, we prese…
The Role of Data Analysis in Uncertainty Quantification: Case Studies for Materials Modeling
Paul N. Patrone, Anthony J. Kearsley, Andrew M. Dienstfrey
In computational materials science, mechanical properties are typically extracted from simulations by means of analysis routines that seek to mimic their experimental counterparts.…
Diffusion-limited Reactions in Nanoscale Electronics
Ryan M. Evans, Arvind Balijepalli, Anthony J. Kearsley
A partial differential equation (PDE) was developed to describe time-dependent ligand-receptor interactions for applications in biosensing using field effect transistors (FET). The…