The deep learning ensemble technique combined with CNN for human breast cancer prognosis prediction.
Our manuscipt titled with "Multi-modal classification for human breast cancer prognosis prediction: Proposal of deep-learning based stacked ensemble model" has been accepted at IEEE/ACM Transactions on Computational Biology and Bioinformatics.
cnn_clinical.py
cnn_cnv.py
cnn_exp.py
STACKED_RF_HIDDEN.model
ttest.py
=> Run cnn_clinical.py, cnn_cnv.py, cnn_exp.py for training individual CNNs for clinical, CNA and gene-expression data.
=> After successfull run you will get the hidden features in three different csv files : clinical_metadata.csv, cnv_metadata.csv and exp_metadata.csv
=> Combine all the hidden features of different modalities to form stacked features : stacked_metadata.csv
=> run RF.py and pass the stacked feature(stacked_metadata.csv) as input to get the final prediction output.
=> Once final prediction has been made use ttest.py to perform statistical significance test.