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Protein function prediction with high-throughput data

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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Correspondence to Kazuyuki Aihara.

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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 (2008). https://doi.org/10.1007/s00726-008-0077-y

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Keywords

  • High-throughput data
  • Machine learning
  • Protein function prediction
  • Semi-supervised learning
  • Supervised learning
  • Unsupervised learning