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Research Article

An Integrative Computational Framework Based on a Two-Step Random Forest Algorithm Improves Prediction of Zinc-Binding Sites in Proteins

  • Cheng Zheng equal contributor,

    equal contributor Contributed equally to this work with: Cheng Zheng, Mingjun Wang

    Affiliation: National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China

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  • Mingjun Wang equal contributor,

    equal contributor Contributed equally to this work with: Cheng Zheng, Mingjun Wang

    Affiliation: National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China

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  • Kazuhiro Takemoto,

    Affiliation: Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan

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  • Tatsuya Akutsu,

    Affiliation: Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan

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  • Ziding Zhang mail,

    zidingzhang@cau.edu.cn (ZZ); Jiangning.Song@monash.edu (JS)

    Affiliation: State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China

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  • Jiangning Song mail

    zidingzhang@cau.edu.cn (ZZ); Jiangning.Song@monash.edu (JS)

    Affiliations: National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan, Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, Victoria, Australia

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  • Published: November 14, 2012
  • DOI: 10.1371/journal.pone.0049716

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