Abstract
This paper delivers an example of applying intelligent data analysis to biological data where the success of the project was only possible due to joint efforts of the experts from biology, medicine and data analysis. The initial and seemingly obvious approach for the analysis of the data yielded results that did not look plausible to the biologists and medical doctors. Only a better understanding of the experimental setting and the data generating process enabled us to develop a more suitable model for the underlying experiments and to provide results that are coherent with what could be expected from our knowledge and experience.
The data analysis problem we discuss here is the identification of significant changes in experiments with short hairpin RNA. A simple Monte Carlo test yielded incoherent results and it turned out that the assumptions on the underlying experiments were not justified. With a Bayesian approach incorporating necessary prior knowledge from the biologists, we could finally solve the problem.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Hand, D.J., Berthold, M. (eds.): Intelligent Data Analysis: An Introduction, 2nd edn. Springer, Berlin (2009)
Dickins, R.A., Hemann, M.T., Zilfou, J.T., Simpson, D.R., Ibarra, I., Hannon, G.J., Lowe, S.W.: Probing tumor phenotypes using stable and regulated synthetic microRNA precursors. Nat. Genet. 37, 1289–1295 (2005)
Silva, J.M., Li, M.Z., Chang, K., Ge, W., Golding, M.C., Rickles, R.J., Siolas, D., Hu, G., Paddison, P.J., Schlabach, M.R.: Second-generation shRNA libraries covering the mouse and human genomes. Nat. Genet. 33, 1281–1288 (2005)
Paddison, P., Caudy, A., Bernstein, E., Hannon, G., Conklin, D.: Short hairpin rnas (shrnas) induce sequence-specific silencing in mammalian cells. Genes Dev. 16, 948–958 (2002)
Zhu, L.: Nonparametric Monte Carlo Tests and Their Applications. Springer, New York (2005)
Shaffer, J.P.: Multiple hypothesis testing. Ann. Rev. Psych. 46, 561–584 (1995)
Jaynes, E.T.: Probability Theory: The Logic of Science. Cambridge University Press, Cambridge (2003)
O’Hagan, A., Forster, J.: Bayesian Inference, 2nd edn. Oxford University Press, Oxford (2003)
Cestnik, B.: Estimating probabilities: A crucial task in machine learning. In: Aiello, L.C. (ed.) Proceedings of the ninth European Conference on Artificial Intelligence, pp. 147–149 (1990)
Good, I.J.: The Estimation of Probabilities: An Essay on Modern Bayesian Methods. MIT Press, Cambridge (1965)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Klawonn, F., Wüstefeld, T., Zender, L. (2010). Statistical Modelling for Data from Experiments with Short Hairpin RNAs. In: Cohen, P.R., Adams, N.M., Berthold, M.R. (eds) Advances in Intelligent Data Analysis IX. IDA 2010. Lecture Notes in Computer Science, vol 6065. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13062-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-13062-5_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13061-8
Online ISBN: 978-3-642-13062-5
eBook Packages: Computer ScienceComputer Science (R0)