Abstract—Predicting membrane protein types is an important and challenging research in current molecular and cellular biology. The knowledge of membrane proteins types often provides crucial hints for determining the function of uncharacterized membrane proteins. It is thus highly desirable to develop an automated method that can serve as a high throughput tool in identifying the types of newly found membrane proteins by their primary sequence information only. In this paper, features are extracted from membrane protein sequences using pseudo-amino acid (PseAA) composition. An ensemble classification approach is developed using K-nearest neighbor and Probabilistic Neural Network as the basic learning mechanisms. Each basic classifier is trained using PseAA composition with different tiers. The success rate has been obtained by the ensemble classifier on all the tests such as self-consistency, jackknife, and independent dataset test is quite promising and indicating that the ensemble classifier may become a useful and high performance tool in identifying membrane proteins and their types.

Index Terms—Ensemble classification, K-nearest neighbor, pseudo-amino acid (PseAA) composition, probabilistic neural network.

The authors are with Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad, Pakistan (e-mail: Maqsood.hayat@gmail.com, asif@pieas.edu.pk).