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drug_combination_crc's Introduction

A heuristic approach to identify cancdidate drugs to use in combination for CRC treatment

Data Processing

  • The notebook EDA contains the code to process the raw drugs, cell lines gene expression, and mutation data for downstream procedures

Train FastJTNNpy3 : Junction Tree Variational Autoencoder for Molecular Graph Generation

  • The TrainJTNN notebook in FastJTNNpy3 folder contains the code to train the Junction Tree Variational Autoencoder and obtain the latent representation for the drugs

Train GeneVAE : an Overparameterized Autoencoder for cell line gene expression profile encoding

  • The Train_GeneAE notebook contains the code to train the autoencoder and obtain the latent representation of the cell lines

Train MLP to predict the log2-fold change vialbility in a cell line given a drug

  • The MLP notebook contains the code to train the model
  • The Inference notebook contains the code to evaluate the model and run the heursitic algorith to identify candidate drugs

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