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Discovering Protein Functional Models Using Inductive Logic Programming

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Knowledge Discovery and Data Mining. Current Issues and New Applications (PAKDD 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1805))

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Abstract

The paper describes a method for machine discovery of protein functional models from protein databases using Inductive Logic Programming based on top-down search for relative least general generalization. The method discovers effectively protein function models that explain the relationship between functions of proteins and their amino acid sequences described in protein databases. The method succeeds in discovering protein functional models for forty membrane proteins, which coincide with conjectured models in literature of molecular biology.

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References

  1. Attwood, T. K. and Parry-Smith, D. J.: Introduction to bioinformatics. Longman (1999)

    Google Scholar 

  2. Bairoch, A, Bucher, P., and Hofmann, K.: The PROSITE database, its status in 1997, Nucl. Acids Res., Vol.24, pp.217–221 (1997)

    Article  Google Scholar 

  3. Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (eds.).: Advances in Knowledge Discovery and Data Mining, AAAI Press/The MIT Press (1996)

    Google Scholar 

  4. Futai, M. (ed.): Biomembrane Engineering (in Japanese), Maruzen (1991)

    Google Scholar 

  5. Ishikawa, T., Mitaku, S., Terano, T., Hirokawa, T., Suwa, M., and Seah, B-C.: Building A Knowledge-Base for Protein Function Prediction using Multistrategy Learning, In Proceedings of Genome Informatics Workshop 1995, pp.39–48 (1995)

    Google Scholar 

  6. Ishikawa, T., Terano, T. and Numao, M.: A Computation Method of Relative Least General Generalization Using Literal Association and MDL Criteria, Journal of Japanese Society for Artificial Intelligence (in Japanese), Vol.14, No. 2, pp.326–333 (1999)

    Google Scholar 

  7. Muggleton, S. and Feng, C.: Efficient Induction of Logic Programs, In Proceedings of the 1st Conference on Algorithmic Learning Theory, Ohmsha (1990)

    Google Scholar 

  8. Muggleton, S., King, R., and Sternberg, M.: Protein Secondary Structure Prediction using Logic., Protein Engineering, Vol.5, pp.647–657 (1992)

    Article  Google Scholar 

  9. Muggleton, S. and De Raedt, L.: Inductive Logic Programming: Theory and Methods, The Journal of Logic Programming, Vol.19, pp.629–679 (1994)

    Article  Google Scholar 

  10. Muggleton, S.: Inverse Entailment and Progol. New Generation Computing, Vol.13, pp.245–286 (1995)

    Article  Google Scholar 

  11. Quinlan, R.: Learning Logical Definition from Relations, Machine Learning, Vol.5, pp.239–266 (1990)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Information and Computer Eng., Kisarazu National College of Technology, Chiba, 292-0041, Japan

    Takashi Ishikawa

  2. Dept. of Computer Sci., Faculty of Eng., Tokyo Institute of Technology, Tokyo, 152-8552, Japan

    Masayuki Numao

  3. Graduate School of Systems Management, The University of Tsukuba, Tokyo, 112-0012, Japan

    Takao Terano

Authors
  1. Takashi Ishikawa
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  2. Masayuki Numao
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  3. Takao Terano
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Editor information

Editors and Affiliations

  1. Graduate School of Systems Management, Universiy of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, Tokyo, 112-0012, Japan

    Takao Terano

  2. Department of Computer Science and Engineering, Arizona State University, P.O. Box 875 406, Tempe, AZ, 85287-5406

    Huan Liu

  3. Department of Computer Science, National Tsing Hua University, Hsinchu, 300, Taiwan ROC

    Arbee L. P. Chen

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© 2000 Springer-Verlag Berlin Heidelberg

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Ishikawa, T., Numao, M., Terano, T. (2000). Discovering Protein Functional Models Using Inductive Logic Programming. In: Terano, T., Liu, H., Chen, A.L.P. (eds) Knowledge Discovery and Data Mining. Current Issues and New Applications. PAKDD 2000. Lecture Notes in Computer Science(), vol 1805. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45571-X_27

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  • DOI: https://doi.org/10.1007/3-540-45571-X_27

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67382-8

  • Online ISBN: 978-3-540-45571-4

  • eBook Packages: Springer Book Archive

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