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Project:
Cellular-Level Pilot
Description:
Builds a sparse autoencoder that can compress the expression profile of a sample of gene expression data into a low-dimensional vector.
DESCRIPTION:
Builds a sparse autoencoder that can compress the expression profile of a sample of gene expression data into a low-dimensional vector.
IMPACT: Offers an autoencoder to collapse high-dimensional expression profiles into low-dimensional vectors without significant loss of information.
INPUT DATA TYPE: Gene Expression
INPUT DATA FORMAT: Tabular
LEVEL OF DOCUMENTATION: Minimal
AVAILABLE ON GITHUB
Project:
Cellular-Level Pilot
Description:
Shows how to train and use a neural network model to predict tumor dose response.
DESCRIPTION:
Shows how to train and use a neural network model to predict tumor dose response.
IMPACT: Enables prediction of growth percentage of a cell line treated with a new drug.
INPUT DATA TYPE: Drug Molecular Descriptors, Drug Concentrations, Gene Expression
INPUT DATA FORMAT: Tabular
LEVEL OF DOCUMENTATION: Minimal
AVAILABLE ON GITHUB
