##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.