Integrating Second-order Moving Average and Over-sampling Algorithm to Predict Apoptosis Protein Subcellular Localization

Author:

Liang Yunyun1,Zhang Shengli2

Affiliation:

1. School of Science, Xi’an Polytechnic University, Xi’an 710048, China

2. School of Mathematics and Statistics, Xidian University, Xi’an 710071, China

Abstract

Background: Apoptosis proteins have a key role in the development and the homeostasis of the organism, and are very important to understand the mechanism of cell proliferation and death. The function of apoptosis protein is closely related to its subcellular location. Objective: Prediction of apoptosis protein subcellular localization is a meaningful task. Methods: In this study, we predict the apoptosis protein subcellular location by using the PSSMbased second-order moving average descriptor, nonnegative matrix factorization based on Kullback-Leibler divergence and over-sampling algorithms. This model is named by SOMAPKLNMF- OS and constructed on the ZD98, ZW225 and CL317 benchmark datasets. Then, the support vector machine is adopted as the classifier, and the bias-free jackknife test method is used to evaluate the accuracy. Results: Our prediction system achieves the favorable and promising performance of the overall accuracy on the three datasets and also outperforms the other listed models. Conclusion: The results show that our model offers a high throughput tool for the identification of apoptosis protein subcellular localization.

Funder

National Natural Science Foundation of China

National Basic Research Program of China

Doctoral Scientific Research Start-up Foundation from Henan University of Technology

Publisher

Bentham Science Publishers Ltd.

Subject

Computational Mathematics,Genetics,Molecular Biology,Biochemistry

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