The ATOM Modeling PipeLine (AMPL) is an open-source, modular, extensible software pipeline for building and sharing models to advance in silico drug discovery.

ECP-CANDLE Benchmarks

Multiscale Machine-learned Modeling Infrastructure (MuMMI) is a methodology that the Pilot 2 team has developed to study the interaction of active KRAS with the plasma membrane (and related phenomena) on very large temporal and spatial scales.

ECP-CANDLE level documentation

Image Generator for Tabular Data (IGTD): Converting Tabular Data to Images for Deep Learning Using Convolutional Neural Networks

Classifying RNA-seq samples into different tumor types.

MT-CNN is a CNN for Natural Language Processing and Information Extraction from free-form texts. BSEC group designed the model for information extraction from cancer pathology reports.

Dynamic Importance Sampling

Modified version for TULIP to classify Canine RNA-seq samples into different tumor types.

Using the Random Forest machine learning algorithm to predict the concentration, cell line- and drug-dependent response function.

Hierarchical attention networks for information extraction from cancer pathology reports.

Learning curves

Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Drug Response Prediction Models