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T. Huang, P. Wang, Z.-Q. Ye, H. Xu, Z. He, K.-Y. Feng, L. Hu, W. Cui, K. Wang, X. Dong, L. Xie, X. Kong, Y.-D. Cai and Y. Li, “Prediction of Deleterious Non-Synonymous SNPs Based on Protein Interaction Network and Hybrid Properties,” PloS One, Vol. 5, 2010.

has been cited by the following article:

• TITLE: Identification of Deleterious Single Amino Acid Polymorphism Using Sequence Information Based on Feature Selection and Parameter Optimization

  AUTHORS: Xiao Chen, Qinke Peng, Jia Lv

  KEYWORDS: Single Amino Acid Polymorphisms; Support Vector Machine; Univariate Marginal Distribution Algorithm

  JOURNAL NAME: Engineering, Vol.5 No.10B, December 18, 2013

  ABSTRACT: Most of the human genetic variations are single nucleotide polymorphisms (SNPs), and among them, non-synonymous SNPs, also known as SAPs, attract extensive interest. SAPs can be neural or disease associated. Many studies have been done to distinguish deleterious SAPs from neutral ones. Since many previous studies were based on both structural and sequence features of the SAP, these methods are not applicable when protein structures are not available. In the current paper, we developed a method based on UMDA and SVM using protein sequence information to predict SAP’s disease association. We extracted a set of features that are independent of protein structure for each SAP. Then a SVM-based machine-learning classifier that used grid search to tune parameters was applied to predict the possible disease associa-tion of SAPs. The SVM method reaches good prediction accuracy. Since the input data of SVM contain irrelevant and noisy features and parameters of SVM also affect the prediction performance, we introduced UMDA-based wrapper approach to search for the ‘best’ solution. The UMDA-based method greatly improved prediction performance. Com-pared with current method, our method achieved better performance.