Builds a deep learning network to classify the cancer type from patient Somatic SNPs.

Classifies tumor type; augments existing data quality control methods.

This is a community model for drug response prediction curated as part of the IMPROVE project.

The QMugs HOMO-LUMO Gap Prediction Model enables users to predict HOMO-LUMO energy gap.

Generates synthetic biomedical text of desired clinical context.

Machine-learning models made to study safety parameters using large datasets, which include raw data from DTC, ChEMBL, and ExCAPE-DB databases, as well as the Union train/test dataset. The corresponding Union models are trained on each Union training set with an open-source software called the ATOM Modeling PipeLine (AMPL).

Shows how to train and use a neural network model to predict tumor dose response.

Allows classification of tumor type based on sequence data; these augment existing data quality control methods.

This is a community model for drug response prediction curated as part of the IMPROVE project.

Allows predictive classification of primary tumor type based on gene expression data.