Prediction of essential proteins based on gene expression programming

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  author =       "Jiancheng Zhong and Jianxin Wang and Wei Peng and 
                 Zhen Zhang and Yi Pan",
  title =        "Prediction of essential proteins based on gene
                 expression programming",
  journal =      "BMC Genomics",
  year =         "2013",
  volume =       "14",
  number =       "(Suppl 4)",
  pages =        "S7",
  month =        oct # "~01",
  note =         "Selected articles from the IEEE International
                 Conference on Bioinformatics and Biomedicine 2012:
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  ISSN =         "1471-2164",
  publisher =    "BioMed Central Ltd.",
  bibsource =    "OAI-PMH server at",
  language =     "en",
  oai =          "",
  type =         "Research",
  URL =          "",
  DOI =          "doi:10.1186/1471-2164-14-S4-S7",
  abstract =     "Abstract Background Essential proteins are
                 indispensable for cell survive. Identifying essential
                 proteins is very important for improving our
                 understanding the way of a cell working. There are
                 various types of features related to the essentiality
                 of proteins. Many methods have been proposed to combine
                 some of them to predict essential proteins. However, it
                 is still a big challenge for designing an effective
                 method to predict them by integrating different
                 features, and explaining how these selected features
                 decide the essentiality of protein. Gene expression
                 programming (GEP) is a learning algorithm and what it
                 learns specifically is about relationships between
                 variables in sets of data and then builds models to
                 explain these relationships. Results In this work, we
                 propose a GEP-based method to predict essential protein
                 by combing some biological features and topological
                 features. We carry out experiments on S. cerevisiae
                 data. The experimental results show that the our method
                 achieves better prediction performance than those
                 methods using individual features. Moreover, our method
                 outperforms some machine learning methods and performs
                 as well as a method which is obtained by combining the
                 outputs of eight machine learning methods. Conclusions
                 The accuracy of predicting essential proteins can been
                 improved by using GEP method to combine some
                 topological features and biological features.",

Genetic Programming entries for Jiancheng Zhong Jianxin Wang Wei Peng Zhen Zhang Yi Pan