Skip to main content
Springer Nature Link
Log in

Investigation of Average Mutual Information for Species Separation Using GSOM

  • Conference paper
Future Generation Information Technology (FGIT 2009)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 5899))

Included in the following conference series:

Abstract

The average mutual information (AMI) has been claimed to be a strong genome signature in some literatures. The range of k values is an important parameter in AMI but no standard range of k value is yet proposed. We introduce a new growth threshold (GT) equation in Growing Self-Organising Maps (GSOM) to identify the best k range for clustering prokaryotic sequence fragments of 10 kb. However, the results using the best k range of AMI were still worse than our previously published results using oligonucleotide frequencies. These experiments showed that the newly proposed GT equation makes GSOM able to efficiently and effectively analyse different data features for the same data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Grosse, I., Herzel, H., Buldyrev, S.V., Stanley, H.E.: Species independence of mutual information in coding and noncoding DNA. Phys. Rev. E. J1 - PRE 61(5), 5624–5629 (2000)

    Article  Google Scholar 

  2. Slonim, N., Elemento, O., Tavazoie, S.: Ab initio genotype-phenotype association reveals intrinsic modularity in genetic networks. Molecular Systems Biology 2 (2006)

    Google Scholar 

  3. Swati, D.: In silico comparison of bacterial strains using mutual information. J. Biosci. 32(6), 1169–1184 (2007)

    Article  Google Scholar 

  4. Otu, H.H., Sayood, K.: A divide-and-conquer approach to fragment assembly. Bioinformatics 19(1), 22–29 (2003)

    Article  Google Scholar 

  5. Bauer, M., Schuster, S.M., Sayood, K.: The average mutual information profile as a genomic signature. BMC Bioinformatics 9(48) (2008), doi:10.1186/1471-2105-1189-1148

    Google Scholar 

  6. Chan, C.-K.K., Hsu, A.L., Tang, S.-L., Halgamuge, S.K.: Using Growing Self-Organising Maps to Improve the Binning Process in Environmental Whole-Genome Shotgun Sequencing. Journal of Biomedicine and Biotechnology 2008, Article ID 513701, 10 (2008)

    Article  Google Scholar 

  7. Alahakoon, L.D., Halgamuge, S.K., Srinivasan, B.: Dynamic self-organizing maps with controlled growth for knowledge discovery. IEEE Transactions on Neural Networks 11(3), 601–614 (2000)

    Article  Google Scholar 

  8. Kohonen, T.: Self-Organizing Maps, 2nd edn. Springer, New York (1997)

    MATH  Google Scholar 

  9. Deschavanne, P., Giron, A., Vilain, J., Dufraigne, C., Fertil, B.: Genomic signature is preserved in short DNA fragments. In: Proceedings of the 1st IEEE International Symposium on Bioinformatics and Biomedical Engineering, pp. 161–167 (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. DOME, Faculty of Engineering, The University of Melbourne, Australia

    Chon-Kit Kenneth Chan & Saman Halgamuge

Authors
  1. Chon-Kit Kenneth Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saman Halgamuge
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Sogang University, South Korea

    Young-hoon Lee

  2. Hannam University, Daejeon, South Korea

    Tai-hoon Kim

  3. National Chiao Tung University, Hsinchu, Taiwan

    Wai-chi Fang

  4. University of Warsaw & Infobright Inc., Poland

    Dominik Ślęzak

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chan, CK.K., Halgamuge, S. (2009). Investigation of Average Mutual Information for Species Separation Using GSOM. In: Lee, Yh., Kim, Th., Fang, Wc., Ślęzak, D. (eds) Future Generation Information Technology. FGIT 2009. Lecture Notes in Computer Science, vol 5899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10509-8_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-10509-8_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10508-1

  • Online ISBN: 978-3-642-10509-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics