Abstract
In this paper we present an hypothesis test of randomness based on the probability density function of the symmetrized Kulback-Leibler distance estimated, via a Monte Carlo simulation, by the distributions of the interval lengths detected using the Multi-Layer Model (MLM). The MLM is based on the generation of several sub-samples of an input signal; in particular a set of optimal cut-set thresholds are applied to the data to detect signal properties. In this sense MLM is a general pattern detection method and it can be considered a preprocessing tool for pattern discovery. At the present the test has been evaluated on simulated signals which respect a particular tiled microarray approach used to reveal nucleosome positioning on Saccharomyces cerevisiae; this in order to control the accuracy of the proposed test of randomness. It has been also applied to real biological data. Results indicate that such statistical test may indicate the presence of structures in the signal with low signal to noise ratio.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Corona, D.F.V., Di Gesù, V., Lo Bosco, G., Pinello, L., Collesano, M., Yuan, G.-C.: A Multi-layer method to study Genome-Scale Positions of Nucleosomes. In: Di Gesù, V., Maccarone, M.C., Lo Bosco, G. (eds.) Proc. of Data Analysis in Astronomy, Modelling and Simulation in Science, Erice, Italy, pp. 169–177 (2007)
Corona, D.F.V., Di Gesù, V., Lo Bosco, G., Pinello, L., Yuan, G.C.: A new Multi-Layers Method to Analyze Gene Expression. In: Apolloni, B., Howlett, R.J., Jain, L. (eds.) KES 2007, Part III. LNCS (LNAI), vol. 4694, pp. 862–869. Springer, Heidelberg (2007)
Di Gesù, V., Lo Bosco, G., Pinello, L.: A one class classifier for Signal identification: a biological case study. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part III. LNCS (LNAI), vol. 5179, pp. 747–754. Springer, Heidelberg (2008)
Yuan, G.-C., Liu, Y.J., Dion, M.F., Slack, M.D., Wu, L.F., Altschuler, S.J., Rando, O.: Genome-Scale Identification of Nucleosome Positions in S. cerevisiae. Science 309, 626–630 (2005)
Johnson, D.H., Sinanović, S.: Symmetrizing the Kullback-Leibler Distance. Technical report, Rice University (March 18, 2001)
Arizono, I., Ohta, H.: A Test for Normality Based on Kullback-Leibler Information. The American Statistician 43(1), 20–22 (1989)
Senoglu, B., Surucu, B.: Goodness-of-Fit Tests Based on Kullback-Leibler Information. IEEE TRANS. on Reliability 53(3), 357–361 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Di Gesù, V., Lo Bosco, G., Pinello, L. (2009). Interval Length Analysis in Multi Layer Model. In: Masulli, F., Tagliaferri, R., Verkhivker, G.M. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2008. Lecture Notes in Computer Science(), vol 5488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02504-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-02504-4_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02503-7
Online ISBN: 978-3-642-02504-4
eBook Packages: Computer ScienceComputer Science (R0)