leekanggeun/CPEM

CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of random forests and a deep neural network

CPEM

CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random foerest and a deep neural network

The paper is available here

DATA

Data is avaliable on below address because of data size. You can download the data and lable in here Somatic alteration information , Cancer type information

Quick start

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We recommend the below argument to use the similar verification method as this paper.

python main.py --epoch=120 --batch_size=16 --lr=1e-3 --inner=10 --outer=10 --ensemble --search --feature_selection

#inner: This argument can be 'LOOCV' (Leave One Out Cross Validation) or 'K' (K should be integer) for K-fold inner cross validation. #outer: This argument can be 'LOOCV' (Leave One Out Cross Validation) or 'K' (K should be integer) for K-fold outer cross validation. #ensemble: Whether you use ensemble model #search: Whether you search the optimized number of features #feature_selection: Whether you execute feature selection

Requirements

1. tensorflow-gpu>=2.1.0
2. scikit-learn>=0.18.1
3. tensorflow-addons == 0.9.0
4. hdf5sotrage

Future work

To increase the performance of feature selection, we plan to modify the feature selection code to Tensorflow.