Title: Prediction of Subcellular Location of Apoptosis Proteins Using Pseudo Amino Acid Composition: An Approach from Auto Covariance Transformation
Volume: 17
Issue: 10
Author(s): Taigang Liu, Xiaoqi Zheng, Chunhua Wang and Jun Wang
Affiliation:
Keywords:
Apoptosis protein, auto covariance transformation, jackknife cross-validation test, position-specific score matrix, subcellular location, support vector machine
Abstract: Knowledge of apoptosis proteins plays an important role in understanding the mechanism of programmed cell death. Thus, annotating the function of apoptosis proteins is of significant value. Since the function of apoptosis proteins correlates with their subcellular location, the information about their subcellular location can be very useful in understanding their role in the process of programmed cell death. In the present study, we propose a novel sequence representation that incorporates the evolution information represented in the position-specific score matrices by the auto covariance transformation. Then the support vector machine classifier is adopted to predict subcellular location of apoptosis proteins. To verify the performance of this method, jackknife cross-validation tests are performed on three widely used benchmark datasets and results show that our approach achieves relatively high prediction accuracies over some classical methods.
