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Bioinformatics Advance Access published online on January 5, 2006
Bioinformatics, 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
Received January 6, 2005
Revised December 10, 2005
Accepted December 23, 2005

Article

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

Amato R. 1, Ciaramella A. 2, Deniskina N. 3, Del Mondo C. 1, di Bernardo D. 4, Donalek C. 5, Longo G. 6, Mangano G. 7, Miele G. 8, Raiconi G. 9, Staiano A. 10, and Tagliaferri R. 9 *

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

* To whom correspondence should be addressed.
Tagliaferri R., E-mail: robtag{at}unisa.it


  Abstract

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 data set 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.

Associate Editor: Martin Bishop
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