Publications

Publications

Bayesian prognostic covariate adjustment

Our novel method - Bayesian prognostic covariate adjustment - is a Bayesian analysis that draws on the strengths of the prognostic model approach.
Publications

Modeling Disease Progression in Mild Cognitive Impairment and Alzheimer's Disease with Digital Twins

Here, we have demonstrated that a particular type of generative model (i.e., CRBMs) can be used to accurately model disease progression for patients with MCI or AD.
Publications

Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score

Linear adjustment for a prognostic score is an effective and safe method for leveraging historical data to reduce uncertainty in randomized trials.
Publications

Using Digital Twins to Decrease Enrollment and Increase Statistical Power in Alzheimer's Disease Trials (CTAD 2020)

We showed that digital twins could reduce the number of control subjects required in the analysis to achieve equivalent results to an analysis of the actual subjects.
Publications

Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data

The ability to confidently predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics.
Publications

Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks

Using a dataset of subjects enrolled in the placebo arms of MS clinical trials, we trained a Conditional Restricted Boltzmann Machine to generate digital subjects.
Publications

Generating Digital Control Subjects using Machine Learning for Alzheimer's Disease Clinical Trials (CTAD 2019)

The ability to reduce the burden on control subjects with subjects in clinical trials for complex diseases like Alzheimer’s Disease would drastically improve the search..
Publications

Digital Control Subjects for Alzheimer's Disease Clinical Trials (AMIA 2019)

The ability to simulate dozens of clinical characteristics simultaneously is a powerful tool to model disease progression.
Publications

Machine learning for comprehensive forecasting of Alzheimer's Disease progression

We have shown that generative models capable of sampling conditional probability distributions over a diverse array of clinical variables can accurately model the...
Publications

Synthetic Control Subjects for Alzheimer's Disease Clinical Trials (JSM 2019)

To develop a method to model disease progression that simulates detailed clinical data records for subjects in the control arms of Alzheimer's disease clinical trials.
Publications

Synthetic Control Subjects for Alzheimer's Disease Clinical Trials (AAIC 2019)

The ability to simulate dozens of clinical characteristics simultaneously is a powerful tool to model disease progression.
Publications

Generating Synthetic Control Subjects Using Machine Learning for Clinical Trials in Alzheimer's Disease (DIA 2019)

The ability to simulate dozens of patient characteristics simultaneously is a powerful tool to model disease progression.
Publications

Deep Learning of Representations for Transcriptomics-based Phenotype Prediction

The ability to predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics.
Publications

A high-bias, low-variance introduction to Machine Learning for physicists

Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application.
Publications

Deep learning for comprehensive forecasting of Alzheimer's Disease progression

Most approaches to machine learning from electronic health data can only predict a single endpoint.
Publications

Who is this gene and what does it do? A toolkit for munging transcriptomics data in python

Transcriptional regulation is extremely complicated. Unfortunately, so is working with transcriptional data.
Publications

Boltzmann Encoded Adversarial Machines

The deep learning revolution has driven tremendous advances on supervised learning problems, and a primary outcome is that feed-forward neural networks have become a powerful tool.