This is the corresponding repository of the paper titled "Prediction of anti-inflammatory peptides by a sequence-based stacking ensemble model named AIPStack".
The AIPStack is a two-layer stacking ensemble model, proposed for the identification of Anti-inflammatory peptides. In this model, the peptide sequences are represented by the combination of two feature encoding schemes, i.e. dipeptide deviation from expected mean and composition of k-spaced amino acid pairs. To construct the prediction framework, random forest and extremely randomized tree are employed as the base-classifiers in the first layer and logistic regression is applied as a meta-classifier in the second layer, which accepts the outputs from the first layer. The systematic workflow for the prediction of AIPs is depicted in the figure below.
The code and datasets are only allowed for accedemic research. Commercial usage is not granted.