Offers an active learning framework for natural language processing of pathology reports to reduce the amount of labelled data required to effectively train a model.

Offers an open source, modular, extensible software pipeline for building and sharing models to advance in silico drug discovery.

Identifies the saliency of hidden nodes in autoencoders by ranking hidden nodes in the latent layer of the autoencoder according to their capability of performing a learning task.

Improves machine/deep learning models by performing hyperparameter optimization.

Performs “dynamic” sampling where the input distribution can change over time and the sampling adapts itself to the new distribution.

Extends the original COXEN method to select genes that are predictive of the efficacies of multiple drugs for building general drug response prediction models that are not specific to a particular drug.

The Framework for Exploring Scalable Computational Oncology (FrESCO) is a modular deep learning natural language processing (NLP) library for extracting structured information from clinical text documents and classifying information to a given data standards.

Transforms tabular data into images by assigning features to pixel positions so that similar features are close to each other in the image.

Allows evaluation of a supervised learning model to determine if it can be further improved with more training data.

Supports very large and multiscale simulations of molecular dynamic interactions between proteins (or their domains) with each other or with cell membranes.