##StackACPred: prediction of anticancer peptides by integrating optimized multiple feature descriptors with stacked ensemble approach
##StackACPred uses the following dependencies:
- Python 3.6
- numpy
- scipy
- scikit-learn
- pandas
- matplotlib
- MATLAB (R2018a)
##Guiding principles: **The dataset folder contain both ACP740 and ACP240 dataset.
**feature extraction:
- PAAC.m directory contains PseAAC.m.
- PsePSSM.m contains PsePSSM.m.
- Seg-PSSM contain Seg-PSSM.m.
** feature selection:
- SVM-RFE+CBR.
** Classifier:
- stacking.py implements stacked ensemble classifier.
** Dataset:
- ACP740_dataset_.txt contains the data of the ACP and non-ACP dataset.
- ACP240_dataset_.txt contains the data of the ACP and non-ACP dataset.