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

• Cheng Zheng ,

  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 ,

  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 ,

  * E-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

  * E-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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