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Prediction of protein-protein interactions using chaos game representation and wavelet transform via the random forest algorithm.
Jia, J H; Liu, Z; Chen, X; Xiao, X; Liu, B X.
Afiliação
  • Jia JH; School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China jjh163yx@163.com.
  • Liu Z; School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China.
  • Chen X; School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China.
  • Xiao X; School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China.
  • Liu BX; School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China.
Genet Mol Res ; 14(4): 11791-805, 2015 Oct 02.
Article em En | MEDLINE | ID: mdl-26436504
Studying the network of protein-protein interactions (PPIs) will provide valuable insights into the inner workings of cells. It is vitally important to develop an automated, high-throughput tool that efficiently predicts protein-protein interactions. This study proposes a new model for PPI prediction based on the concept of chaos game representation and the wavelet transform, which means that a considerable amount of sequence-order effects can be incorporated into a set of discrete numbers. The advantage of using chaos game representation and the wavelet transform to formulate the protein sequence is that it can more effectively reflect its overall sequence-order characteristics than the conventional correlation factors. Using such a formulation frame to represent the protein sequences means that the random forest algorithm can be used to conduct the prediction. The results for a large-scale independent test dataset show that the proposed model can achieve an excellent performance with an accuracy value of about 0.86 and a geometry mean value of about 0.85. The model is therefore a useful supplementary tool for PPI predictions. The predictor used in this article is freely available at http://www.jci-bioinfo.cn/PPI.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Proteínas de Bactérias / Modelos Estatísticos / Proteínas de Saccharomyces cerevisiae / Mapeamento de Interação de Proteínas / Análise de Ondaletas Tipo de estudo: Clinical_trials / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Genet Mol Res Assunto da revista: BIOLOGIA MOLECULAR / GENETICA Ano de publicação: 2015 Tipo de documento: Article País de afiliação: China País de publicação: Brasil
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Proteínas de Bactérias / Modelos Estatísticos / Proteínas de Saccharomyces cerevisiae / Mapeamento de Interação de Proteínas / Análise de Ondaletas Tipo de estudo: Clinical_trials / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Genet Mol Res Assunto da revista: BIOLOGIA MOLECULAR / GENETICA Ano de publicação: 2015 Tipo de documento: Article País de afiliação: China País de publicação: Brasil