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Enzymes and proteins are live driven biochemicals, which has a dramaticimpact over the environment, in which it is active. So, therefore, it is highlylooked-for to build such a robust and highly accurate automatic andcomputational model to accurately predict enzymes nature. In this study, anovel split amino acid composition model named piSAAC is proposed. In thismodel, protein sequence is discretized in equal and balanced terminus to fullyevaluate the intrinsic correlation properties of the sequence. Severalstate-of-the-art algorithms have been employed to evaluate the proposed model.A 10-folds cross-validation evaluation is used for finding out the authenticityand robust-ness of the model using different statistical measures e.g.Accuracy, sensitivity, specificity, F-measure and area un-der ROC curve. Theexperimental results show that, probabilistic neural network algorithm withpiSAAC feature extraction yields an accuracy of 98.01%, sensitivity of 97.12%,specificity of 95.87%, f-measure of 0.9812and AUC 0.95812, over dataset S1,accuracy of 97.85%, sensitivity of 97.54%, specificity of 96.24%, f-measure of0.9774 and AUC 0.9803 over dataset S2. Evident from these excellent empiricalresults, the proposed model would be a very useful tool for academic researchand drug designing related application areas.

piSAAC: Extended notion of SAAC feature selection novel method for discrimination of Enzymes model using different machine learning algorithm