Skip to main content

Prediction of Combinatorial Protein-Protein Interaction Networks from Expression Data Using Statistics on Conditional Probability

  • Conference paper
  • 1286 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6278))

Abstract

In this paper we propose a method to retrieve combinatorial protein-protein interaction to predict the interaction networks from protein expression data based on statistics on conditional probability. Our method retrieves the combinations of three proteins A, B and C which include combinatorial effects among them. The combinatorial effect considered in this paper does not include the ”sole effect” between two proteins A-C or B-C, so that we can retrieve the combinatorial effect which appears only when proteins A, B and C get together. We evaluate our method with a real protein expression data set and obtain several combinations of three proteins in which protein-protein interactions are prediced.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Enright, A.J., Skrabanek, L., Bader, G.: Computational Prediction of Protein-Protein Interactions. In: The Proteomics Protocols Handbook, pp. 629–652. Humana Press (2005)

    Google Scholar 

  2. Wang, L., Chu, F., Xie, W.: Accurate Cancer Classification Using Expressions of Very Few Genes. IEEE/ACM Transactions on Computational Biology and Bioinformatics 4(1), 40–53 (2007)

    Article  MathSciNet  Google Scholar 

  3. Imoto, S., Goto, T., Miyano, S.: Estimation of genetic networks and functional structures between genes by using Bayesian network and nonparametric regression. In: Pacific Symposium on Biocomputing, vol. 7, pp. 175–186 (2002)

    Google Scholar 

  4. Akutsu, T., Kuhara, S., Maruyama, O., Miyano, S.: Identification of genetic networks by strategic gene disruptions and gene overexpressions under a boolean model. Theoretical Computer Science 298, 235–251 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  5. Yoshihiro, T., Inoue, E., Nakagawa, M.: Mining Combinatorial Effects on Quantitative Traits from Protein Expression Data. In: 8th Joint Conference on Knowledge-based Software Engineering 2008 (JCKBSE 2008), pp. 359–367 (2008)

    Google Scholar 

  6. Lu, Y., Liu, F., Sanchez, M., Wang, Y.: Interactive Semisupervised Learning for Microarray Analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics 4(2), 190–203 (2007)

    Article  Google Scholar 

  7. DeRisi, J.L., Lyer, V.R., Brown, P.O.: Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale. Science 278, 680–686 (1997)

    Article  Google Scholar 

  8. Nagai, K., Yoshihiro, T., Inoue, E., Ikegami, H., Sono, Y., Kawaji, H., Kobayashi, N., Matsuhashi, T., Ohtani, T., Morimoto, K., Nakagawa, M., Iritani, A., Matsumoto, K.: Developing an Integrated Database System for the Large-scale Proteomic Analysis of Japanese Black Cattle. Animal Science Journal 79(4), 467–481 (2008)

    Google Scholar 

  9. Lu, C.: Improving the Scaling Normalization for High-density Oligonucleotide GeneChip Expression Microarrays. BMC Bioinformatics 5(103) (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Fujiki, T., Inoue, E., Yoshihiro, T., Nakagawa, M. (2010). Prediction of Combinatorial Protein-Protein Interaction Networks from Expression Data Using Statistics on Conditional Probability. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15393-8_57

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15393-8_57

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15392-1

  • Online ISBN: 978-3-642-15393-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics