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Protein function prediction with high-throughput data

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Abstract

Protein function prediction is one of the main challenges in post-genomic era. The availability of large amounts of high-throughput data provides an alternative approach to handling this problem from the computational viewpoint. In this review, we provide a comprehensive description of the computational methods that are currently applicable to protein function prediction, especially from the perspective of machine learning. Machine learning techniques can generally be classified as supervised learning, semi-supervised learning and unsupervised learning. By classifying the existing computational methods for protein annotation into these three groups, we are able to present a comprehensive framework on protein annotation based on machine learning techniques. In addition to describing recently developed theoretical methodologies, we also cover representative databases and software tools that are widely utilized in the prediction of protein function.

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Acknowledgments

This work was partly supported by the National High Technology Research and Development Program of China (2006AA02Z309), and JSPS-NSFC collaboration project.

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Authors and Affiliations

  1. ERATO Aihara Complexity Modelling Project, JST, Tokyo, 151-0064, Japan

    Xing-Ming Zhao, Luonan Chen & Kazuyuki Aihara

  2. Institute of Systems Biology, Shanghai University, 200444, Shanghai, China

    Xing-Ming Zhao & Luonan Chen

  3. Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, 230031, Hefei, Anhui, China

    Xing-Ming Zhao

  4. Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan

    Xing-Ming Zhao, Luonan Chen & Kazuyuki Aihara

  5. Department of Electrical Engineering and Electronics, Osaka Sangyo University, Osaka, 574-8530, Japan

    Luonan Chen

Authors
  1. Xing-Ming Zhao
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  2. Luonan Chen
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  3. Kazuyuki Aihara
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Correspondence to Kazuyuki Aihara.

Additional information

This work was partly supported by the National High Technology Research and Development Program of China (2006AA02Z309), and JSPS-NSFC collaboration project.

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Zhao, XM., Chen, L. & Aihara, K. Protein function prediction with high-throughput data. Amino Acids 35, 517–530 (2008). https://doi.org/10.1007/s00726-008-0077-y

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