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Semi-Supervised Multi-View Learning for Gene Network Reconstruction.
Ceci, Michelangelo; Pio, Gianvito; Kuzmanovski, Vladimir; Dzeroski, Saso.
Afiliação
  • Ceci M; Dept. of Computer Science, University of Bari Aldo Moro, Via Orabona 4, 70125 Bari, Italy.
  • Pio G; Dept. of Computer Science, University of Bari Aldo Moro, Via Orabona 4, 70125 Bari, Italy.
  • Kuzmanovski V; Dept. of Knowledge Technologies, Jozef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia.
  • Dzeroski S; Jozef Stefan International Postgraduate School, Jamova 39, 1000 Ljubljana, Slovenia.
PLoS One ; 10(12): e0144031, 2015.
Article em En | MEDLINE | ID: mdl-26641091
The task of gene regulatory network reconstruction from high-throughput data is receiving increasing attention in recent years. As a consequence, many inference methods for solving this task have been proposed in the literature. It has been recently observed, however, that no single inference method performs optimally across all datasets. It has also been shown that the integration of predictions from multiple inference methods is more robust and shows high performance across diverse datasets. Inspired by this research, in this paper, we propose a machine learning solution which learns to combine predictions from multiple inference methods. While this approach adds additional complexity to the inference process, we expect it would also carry substantial benefits. These would come from the automatic adaptation to patterns on the outputs of individual inference methods, so that it is possible to identify regulatory interactions more reliably when these patterns occur. This article demonstrates the benefits (in terms of accuracy of the reconstructed networks) of the proposed method, which exploits an iterative, semi-supervised ensemble-based algorithm. The algorithm learns to combine the interactions predicted by many different inference methods in the multi-view learning setting. The empirical evaluation of the proposed algorithm on a prokaryotic model organism (E. coli) and on a eukaryotic model organism (S. cerevisiae) clearly shows improved performance over the state of the art methods. The results indicate that gene regulatory network reconstruction for the real datasets is more difficult for S. cerevisiae than for E. coli. The software, all the datasets used in the experiments and all the results are available for download at the following link: http://figshare.com/articles/Semi_supervised_Multi_View_Learning_for_Gene_Network_Reconstruction/1604827.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Saccharomyces cerevisiae / Software / Escherichia coli / Redes Reguladoras de Genes / Aprendizado de Máquina / Genes Bacterianos / Genes Fúngicos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2015 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Saccharomyces cerevisiae / Software / Escherichia coli / Redes Reguladoras de Genes / Aprendizado de Máquina / Genes Bacterianos / Genes Fúngicos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2015 Tipo de documento: Article