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Bioinformatics Advance Access originally published online on January 5, 2006
Bioinformatics 2006 22(5):589-596; doi:10.1093/bioinformatics/btk026
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A multi-step approach to time series analysis and gene expression clustering

R. Amato 1, A. Ciaramella 2, N. Deniskina 1,3, C. Del Mondo 1, D. di Bernardo 4, C. Donalek 1,5, G. Longo 1,6,7, G. Mangano 1,6,8, G. Miele 1,6, G. Raiconi 2,6, A. Staiano 1,2 and R. Tagliaferri 2,6,*

1Dipartimento di Scienze Fisiche, University of Naples ‘Federico II’ Naples, ITALY
2Dipartimento di Matematica e Informatica, University of Salerno Fisciano, Salerno, ITALY
3Institute of Information Transmission Problems, Russian Academy of Sciences Moscow, Russia
4Telethon Institute of Genetics and Medicine Naples, ITALY
5Department of Astronomy, California Institute of Technology Pasadena CA, USA
6INFN—Istituto Nazionale Fisica Nucleare Sezione di Napoli, Naples, ITALY
7INAF—Istituto Nazionale di Astrofisica Sezione di Napoli, Naples, ITALY
8Department of Physics, Syracuse University Syracuse NY, USA

*To whom correspondence should be addressed.

Motivation: The huge growth in gene expression data calls for the implementation of automatic tools for data processing and interpretation.

Results: We present a new and comprehensive machine learning data mining framework consisting in a non-linear PCA neural network for feature extraction, and probabilistic principal surfaces combined with an agglomerative approach based on Negentropy aimed at clustering gene microarray data. The method, which provides a user-friendly visualization interface, can work on noisy data with missing points and represents an automatic procedure to get, with no a priori assumptions, the number of clusters present in the data. Cell-cycle dataset and a detailed analysis confirm the biological nature of the most significant clusters.

Availability: The software described here is a subpackage part of the ASTRONEURAL package and is available upon request from the corresponding author.

Contact: robtag{at}unisa.it

Supplementary information: Supplementary data are available at Bioinformatics online.

Received on October 6, 2005; revised on December 10, 2005; accepted on December 23, 2005

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