Feature Combination Methods for Prediction of Subcellular Locations of Proteins with Both Single and Multiple Sites

Abstract

Effective feature extraction methods play very important role for prediction of multisite protein subcellular locations. With the progress of many proteome projects, more and more proteins are annotated with more than one subcellular location. However, compared with the problems of single-site protein, the problems of multiplex protein subcellular localizations are far more difficult and complicated to deal with. To improve the multisite prediction quality, it is necessary to incorporate different feature extraction methods. In this paper, a version of feature combination method which is to make use of the 20 dimensions of entropy density instead of the former 20 dimensions of amphiphilic pseudo amino acid composition (AmPseAAC), is used in two different datasets. It is different from the way of simple dimensions additive feature fusion. On base of this novel feature combination method, we adopt the multi-label k-nearest neighbors (ML-KNN) algorithm and setting different weights into different attributes’ ML-KNN, which is called wML-KNN, to predict multiplex protein subcellular locations. The best overall accuracy rate on dataset S1 from the predictor of Virus-mPLoc is 61.11 % and 82.03 % on dataset S2 from Gpos-mPLoc, respectively.