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Project:
Cellular-Level Pilot
Description:
Provides a semi-supervised, autoencoder-based, machine learning procedure, which learns a smaller set of gene expression features that are robust to batch effects using background information on a cell line or tissue’s tumor type.
DESCRIPTION:
Provides a semi-supervised, autoencoder-based, machine learning procedure, which learns a smaller set of gene expression features that are robust to batch effects using background information on a cell line or tissue’s tumor type.
IMPACT: Dimension reduction of gene expression data using a deep learning algorithm – enables learning about more generalized gene expression features for drug response.
INPUT DATA TYPE: RNA-Seq
INPUT DATA FORMAT: Tabular
LEVEL OF DOCUMENTATION: Minimal
AVAILABLE ON GITHUB
