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Chen H.,Hunan University | Liao B.,Hunan University | Cai L.,Hunan University | Chen X.,Changsha Aeronautical Vocational and Technical College Changsha | Liu S.,Hunan University
Journal of Computational and Theoretical Nanoscience

Although there are many subcellular localization methods currently, the prediction accuracy of them is not good enough. The main reason may be that when converting sequence information into numerical information, these methods lose important information. In order to get more global and local information in the protein sequences, we propose a novel numerical feature extraction method for representation of protein sequences which includes three parts of features that are amino acid composition, compression tripeptide composition and local frequency domain values. Then, we use support vector machine and the nearest neighbor algorithm to predict subcellular localization of two benchmark data sets and compare the prediction results and evaluation index values with other methods. Comparison results prove that our method can effectively extract information of protein sequence and improves the prediction accuracy of subcellular localization. Copyright © 2013 American Scientific Publishers. Source

Li Z.,Hunan Institute of Technology | Li Z.,Hunan University | Yang A.,Hunan University | Chen X.,Changsha Aeronautical Vocational and Technical College Changsha | And 2 more authors.
Journal of Computational and Theoretical Nanoscience

With rapid advancement of microarray technologies, biologists now can analysis thousands of gene expression values at one time. So they can have a 'global' view of the cell. Among all the problems during gene expression data analysis, the curse of dimensionality with thousands of gene expression values but only limited samples, commonly no more than one hundred, becomes more serious. The existence of numerous gene expression data, irrelevant to the classification of tumors, not only increases computational complexity but also makes the discovery of relevant genes impossible. Therefore, feature selection becomes critical important. In this paper, we first use a filter method to rank genes, and then select 'important' genes with high 'information exponential' score. Afterwards, we use a clustering method based on k-NN principle to find truly important genes. A support vector machine is applied to validate the classification performance of candidate genes. The experimental results demonstrate that our method can effective solve the problem caused by filter method, which doesn't consider the relationships among genes. © 2014 American Scientific Publishers. Source

Wang X.,Hunan University | Wen Z.,Hunan University | Li X.,Hunan University | Chen X.,Changsha Aeronautical Vocational and Technical College Changsha | And 2 more authors.
Journal of Computational and Theoretical Nanoscience

Tag single-nucleotide polymorphism (SNP) selection approach is an important tool in computational biology and genome association studies. Numerous approaches have been proposed for selecting an optimal tag SNP set. Most existing tag selection methods only consider the linkage disequilibrium between SNP loci but ignore the relevant among SNPs and disease state. In order to find SNP subset that is the most relevant and the least redundant. In the paper, a sparse representation tag SNP selection algorithm, SR-Tagger, is proposed. The SR-Tagger algorithm contains three steps. Firstly, these SNPs irrelevant to disease state are removed by using sparse representation coefficient. Secondly, a sparse representation-based maximum similarity tree algorithm is used to divide SNPs into clusters. Finally, the most representative SNP with the highest sparse representation coefficient is selected from each cluster as candidate SNPs. Applications to several real datasets from the HapMap project demonstrate that the proposed method significantly improves the efficiency and prediction accuracy of tag SNP selection. © 2016 American Scientific Publishers All rights reserved. Source

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