Lung Cancer Prediction using deep learning with gene expression data

BTP Code - B23SA01

Authors

• Guide - Dr. Amilpur Santhosh
• Akash Yadav - S20200010009
• Sachin Meena - S20200010181
• Seshu Medapi - S20200010124

Data and Preprocessing Block

• The data folder contains data for multiple patients in which each patient have it's own tsv data file containing gene expresion data.

• MergeHelp contains data into csv for each patient.

• preprocess.ipynb contains the Preprocessing code.

• TCGA_Labeled.csv file contains the preprocessed data.

Feature Selection(KL Divergence)

• KL_Divergence jupyter notebook contains code for Kl divegence gene selection.

• Labeled_Final_Features csv file contains the data selected after kl divergence gene selection.

Model

• Training jupyter source file coatains the model code for prediction of lung cancer.

• Results coatains the output after model Training.

Gene Augmentation and Particle Swarm optimization

• Augmentation jupyter source file conatains code for the gene expression data.

• Augmentation_Data1.csv contains data for weighted mixed observations.

• Augmentation_Data2.csv contains data for generative adversarial networks.

• Optimizations jupyter notebook contains the code for particle Swarm optimization.

• TCGA_Labled_PSO constains the data with genes selected after PSO optimization.

• TCGA_Labled_PSO_KL contains the data after pso and kl divergence.

Run Locally

Download the project

Go to the project directory

  cd Btp

Install dependencies

  pip install scikit-learn,pandas,pytorch,matplotlib,seaborn,scipy,tensorflow,shutil

DATA

• You can find all the data and csv's at https://drive.google.com/drive/folders/1X4EUCgavlefy46yt4XpEVxs8hoy5MnXU?usp=drive_link