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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
Project: MOSSAIC
Description: Offers a suite of statistical and machine learning methods to generate discrete/categorical synthetic data.
DESCRIPTION:

Offers a suite of statistical and machine learning methods to generate discrete/categorical synthetic data.

IMPACT: Can produce realistic synthetic clinical data when access to real patient data is limited.
INPUT DATA FORMAT: Tabular
LEVEL OF DOCUMENTATION: Minimal
Project: ADMIRRAL
Description: Computes and analyzes membrane surfaces found in a wide variety of large-scale molecular simulations. MemSurfer works independent of the type of simulation, directly on the 3D point coordinates.
DESCRIPTION:

Computes and analyzes membrane surfaces found in a wide variety of large-scale molecular simulations. MemSurfer works independent of the type of simulation, directly on the 3D point coordinates.

IMPACT: Enables assessment of lipid membrane curvature and density. Allows counting of normal lipids and area per lipid. Provides a simple-to-use Python API to perform other types of analysis.
INPUT DATA TYPE: Membrane Data
INPUT DATA FORMAT: Unspecified
LEVEL OF DOCUMENTATION: Minimal