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Kurgan, L. A. and Homaeian, L. (2006) Prediction of structural classes for protein sequences and domains-Impact of prediction algorithms, sequence representation and homology, and test procedures on accuracy. Pattern Recognition, 39, 2323-2343.

has been cited by the following article:

• TITLE: Using position specific scoring matrix and auto covariance to predict protein subnuclear localization

  AUTHORS: Rong-Quan Xiao, Yan-Zhi Guo, Yu-Hong Zeng, Hai-Feng Tan, Hai-Feng Tan, Xue-Mei Pu, Meng-Long Li

  KEYWORDS: Position Specific Scoring Matrix; Auto Covariance; Support Vector Machine; Protein Subnuclear Localization Prediction

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.2 No.1, February 13, 2009

  ABSTRACT: The knowledge of subnuclear localization in eukaryotic cells is indispensable for under-standing the biological function of nucleus, genome regulation and drug discovery. In this study, a new feature representation was pro-posed by combining position specific scoring matrix (PSSM) and auto covariance (AC). The AC variables describe the neighboring effect between two amino acids, so that they incorpo-rate the sequence-order information; PSSM de-scribes the information of biological evolution of proteins. Based on this new descriptor, a support vector machine (SVM) classifier was built to predict subnuclear localization. To evaluate the power of our predictor, the benchmark dataset that contains 714 proteins localized in nine subnuclear compartments was utilized. The total jackknife cross validation ac-curacy of our method is 76.5%, that is higher than those of the Nuc-PLoc (67.4%), the OET- KNN (55.6%), AAC based SVM (48.9%) and ProtLoc (36.6%). The prediction software used in this article and the details of the SVM parameters are freely available at http://chemlab.scu.edu.cn/ predict_SubNL/index.htm and the dataset used in our study is from Shen and Chou’s work by downloading at http://chou.med.harvard.edu/ bioinf/Nuc-PLoc/Data.htm.