title: Detrended cross-correlation coefficient: Application to predict apoptosis protein subcellular localization.
abstract: Apoptosis, or programed cell death, plays a central role in the development and homeostasis of an organism. Obtaining information on subcellular location of apoptosis proteins is very helpful for understanding the apoptosis mechanism. The prediction of subcellular localization of an apoptosis protein is still a challenging task, and existing methods mainly based on protein primary sequences. In this paper, we introduce a new position-specific scoring matrix (PSSM)-based method by using detrended cross-correlation (DCCA) coefficient of non-overlapping windows. Then a 190-dimensional (190D) feature vector is constructed on two widely used datasets: CL317 and ZD98, and support vector machine is adopted as classifier. To evaluate the proposed method, objective and rigorous jackknife cross-validation tests are performed on the two datasets. The results show that our approach offers a novel and reliable PSSM-based tool for prediction of apoptosis protein subcellular localization.
authors.affiliation: School of Mathematics and Statistics, Xidian University, Xi'an 710071, PR China. Electronic address: yunyunliang88@163.com.
authors.forename: Yunyun
authors.initials: Y
authors.lastname: Liang
authors.affiliation: School of Mathematics and Statistics, Xidian University, Xi'an 710071, PR China.
authors.forename: Sanyang
authors.initials: S
authors.lastname: Liu
authors.affiliation: School of Mathematics and Statistics, Xidian University, Xi'an 710071, PR China.
authors.forename: Shengli
authors.initials: S
authors.lastname: Zhang
mesh_terms: D017209:Apoptosis; D051017:Apoptosis Regulatory Proteins; D008962:Models, Theoretical; D056510:Position-Specific Scoring Matrices; D060388:Support Vector Machine
chemical_list: D051017:Apoptosis Regulatory Proteins
keywords: Detrended cross-correlation coefficient; Position-specific scoring matrix; Subcellular localization; Support vector machine