[Bioclusters] IJCNN 2009 - Tutorial on Introduction to Bioinformatics Data Sets Mining using Fuzzy Biclustering
masulli at disi.unige.it
Sun Mar 29 16:54:00 EDT 2009
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"Introduction to Bioinformatics Data Sets Mining using Fuzzy Biclustering"
International Joint Conference on Neural Networks (IJCNN 2009)
Westin PeachTree Hotel, Atlanta, Georgia, USA, June 14, 2009
Tutorial web site:
IJCNN 2009 web site: http://cnd.memphis.edu/ijcnn2009/
The analysis of genomic data from DNA microarray can produce a valuable
information on the biological relevance of genes and correlations among them.
In the last few years some biclustering techniques have been proposed and
applied to this analysis. Biclustering is an un-supervised learning task aimed
to find clusters of samples possessing similar characteristics together with
features creating these similarities. Starting from the seminal paper by Cheng
and Church published in 2000 , many biclustering techniques have been
proposed for bioinformatic data analysis . Biclustering is especially
useful when applied to the analysis of DNA microarray data since it can tackle
the important problem of identifying genes with similar behavior with respect
to different conditions. Some biological tasks where biclustering can be
successfully applied are: (1) Identification of co-regulated genes and/or
specific regulation processes; (2) Gene functional annotation; (3) Sample
and/or tissue classification. In this tutorial we will focus on the fuzzy model
of biclustering as it is very promising from both a computational and a
representation point of view [4,5,6,7]. This model allows finding multiple
solutions (thus avoiding problems such as random interference ) with
significant speed. Moreover, some techniques, based on the fuzzy-possibilistic
approach to clustering, can find very large and homogeneous biclusters, as
shown by experimental results. In the tutorial we will present also an
experimental assessment of fuzzy biclustering algorithms, using some
computationally parsimonious stability indexes  .
 Y. Cheng and G. M. Church, Biclustering of expression data. Proc Int Conf
Intell Syst Mol Biol, vol. 8, pp. 93-103, 2000.
 S. C. Madeira and A. L. Oliveira, Biclustering algorithms for biological
data analysis: A survey, IEEE Transactions on Computational Biology and
Bioinformatics, vol. 1, pp. 24-45, 2004.
 K. Umayahara, S. Miyamoto, and Y. Nakamori, Formulations of fuzzy
clustering for categorical data, Int. J. of Innovative Computing, Information
and Control, vol. 1, no. 1, pp. 83-94, 2005.
 W.-C. Tjhi and L. Chen, Minimum sum-squared residue for fuzzy co-
clustering, Intelligent Data Analysis, vol. 10, no. 3, pp. 237-249, 2006.
 C. Cano, L. Adarve, J. Lopez, and A. Blanco, Possibilistic approach for
biclustering microarray data, Computers in Biology and Medicine, vol. 37, no.
10, pp. 1426-1436, October 2007.
 M. Filippone, F. Masulli, S. Rovetta, S. Mitra, and H. Banka,
Possibilistic approach to biclustering: An application to oligonucleotide
microarray data analysis.in Lecture Notes in Bioinformatics, C. Priami, Ed.,
vol. 4210. Springer, October 2006, pp. 312-322.
 J. Yang, H. Wang, W. Wang, and P. Yu, Enhanced biclustering on expression
data, in BIBE 03: Proceedings of the 3rd IEEE Symposium on BioInformatics and
BioEngineering. Washington, DC, USA: IEEE Computer Society, 2003, p. 321.
 M. Filippone, F. Masulli, and S. Rovetta, Comparing Fuzzy Approaches to
Biclustering, Computational Intelligence Methods for Bioinformatics and
Biostatistics, Proceedings of the CIBB 2008, LNCS/LNBI, Springer-Verlag,
Heidelberg (Germany), 2008 (in press).
Francesco Masulli (1,2) and Stefano Rovetta (1)
(1) DISI Dept. Computer and Information Sciences
University of Genova and CNISM
Via Dodecaneso 35, 16146 Genoa, Italy
E-mails: masulli <at> disi.unige.it, rovetta <at> disi.unige.it
(3) Sbarro Institute for Cancer Research and Molecular Medicine,
Temple University, 1900 N 12th Street Philadelphia, PA 19122, USA
If interested, please chose this tutorial while registering to IJCNN 2009.
If you already registered to IJCNN2009 and missed to register to this
tutorial, email to inns_registrar at reesgroupinc.com
Dr. Francesco Masulli
Associate Professor of Computer Science
DISI - Dept. Computer and Information Sciences
University of Genova - Via Dodecaneso 35, 16146
Genoa - ITALY
tel. +39 010 353 6604 fax. +39 010 353 6699
Adjunct Associate Professor
Center for Biotechnology - College of Science and
Technology -Temple University - Philadelphia - PA, USA.
email: masulli at disi.unige.it
skype id: masulli
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