Skip to main content
Springer Nature Link
Log in

Cross-Species Translation of Multi-way Biomarkers

  • Conference paper
Artificial Neural Networks and Machine Learning – ICANN 2011 (ICANN 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6791))

Included in the following conference series:

  • 7550 Accesses

Abstract

We present a Bayesian translational model for matching patterns in data sets which have neither co-occurring samples nor variables, but only a similar experiment design dividing the samples into two or more categories. The model estimates covariate effects related to this design and separates the factors that are shared across the data sets from those specific to one data set. The model is designed to find similarities in medical studies, where there is great need for methods for linking laboratory experiments with model organisms to studies of human diseases and new treatments.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

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. Gholami, A.M., Fellenberg, K.: Cross-species common regulatory network inference without requirement for prior gene affiliation. Bioinformatics 26(8), 1082–1090 (2010)

    Article  Google Scholar 

  2. Huopaniemi, I., Suvitaival, T., Nikkilä, J., Orešič, M., Kaski, S.: Multivariate multi-way analysis of multi-source data. Bioinformatics 26, i391–i398 (2010)

    Article  Google Scholar 

  3. Huopaniemi, I., Suvitaival, T., Nikkilä, J., Orešič, M., Kaski, S.: Two-way analysis of high-dimensional collinear data. Data Mining and Knowledge Discovery 19(2), 261–276 (2009)

    Article  MathSciNet  Google Scholar 

  4. Huopaniemi, I., Suvitaival, T., Orešič, M., Kaski, S.: Graphical multi-way models. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6321, pp. 538–553. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  5. Le, H.S., Bar-Joseph, Z.: Cross species expression analysis using a Dirichlet process mixture model with latent matchings. In: Lafferty, J., et al. (eds.) Advances in Neural Information Processing Systems 23, pp. 1270–1278 (2010)

    Google Scholar 

  6. Lu, Y., Huggins, P., Bar-Joseph, Z.: Cross species analysis of microarray expression data. Bioinformatics 25(12), 1476–1483 (2009)

    Article  Google Scholar 

  7. Lucas, J., Carvalho, C., West, M.: A Bayesian analysis strategy for cross-study translation of gene expression biomarkers. Statistical Applications in Genetics and Molecular Biology 8(1), 11 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  8. Mardia, K.V., Bibby, J.M., Kent, J.T.: Multivariate analysis. Academic Press, London (1979)

    MATH  Google Scholar 

  9. Orešič, M., et al.: Dysregulation of lipid and amino acid metabolism precedes islet autoimmunity in children who later progress to type 1 diabetes. Journal of Experimental Medicine 205(13), 2975–2984 (2008)

    Article  Google Scholar 

  10. Tripathi, A., Klami, A., Orešič, M., Kaski, S.: Matching samples of multiple views. Data Mining and Knowledge Discovery (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information and Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University School of Science, Finland

    Tommi Suvitaival, Ilkka Huopaniemi & Samuel Kaski

  2. VTT Technical Research Centre of Finland, Finland

    Matej Orešič

  3. Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Finland

    Samuel Kaski

Authors
  1. Tommi Suvitaival
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ilkka Huopaniemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matej Orešič
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Samuel Kaski
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Information and Computer Science, Aalto University School of Science, P.O. Box 15400, 00076, Aalto, Finland

    Timo Honkela  & Samuel Kaski  & 

  2. School of Physics, Astronomy and Informatics, Department of Informatics, Nicolaus Copernicus University, ul. Grudziadzka 5, 87-100, Torun, Poland

    Włodzisław Duch

  3. Department of Statistical Science, University College London, 1-19 Torrington Place, WC1E 7HB, London, UK

    Mark Girolami

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Suvitaival, T., Huopaniemi, I., Orešič, M., Kaski, S. (2011). Cross-Species Translation of Multi-way Biomarkers. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6791. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21735-7_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-21735-7_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21734-0

  • Online ISBN: 978-3-642-21735-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics