Publications

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

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.

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

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.

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

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.

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..

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

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.

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

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

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

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

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

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

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.