Membrane protein prediction using wavelet decomposition and pseudo amino acid based feature extraction

Membrane proteins play an important role in many biological processes and are attractive drug targets. In this study, membrane proteins are classified using two feature extraction and several classification strategies. The first feature extraction strategy is pseudo amino acid (PseAA) composition; utilizing hydrophobicity and hydrophilicity for reflecting the sequence order effects, while the second method is discrete wavelet analysis (DWT); analyzing the different components of a signal localized both in space and scale domains. The nearest neighbor, probabilistic neural network, support vector machine, random forest, and Adaboost are used as basic learning mechanisms. The predicted results of the base learners are combined using majority voting to form an ensemble classifier. The best accuracy obtained for the Jackknife and independent dataset test is 85.4% and 95.3%, respectively. Using performance measures such as MCC, Sensitivity, Specificity, and F-measure, it has been observed that PseAA based prediction is significantly higher than that of the DWT, and is also the best reported, so far.