[Bioclusters] IJCNN 2009 - Tutorial on Introduction to Bioinformatics Data Sets Mining using Fuzzy Biclustering

[Francesco Masulli]

Please, accept our apologies for multiple postings.
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Tutorial on 
"Introduction to Bioinformatics Data Sets Mining using Fuzzy Biclustering" 
at
International Joint Conference on Neural Networks (IJCNN 2009)
Westin PeachTree Hotel, Atlanta, Georgia, USA, June 14, 2009

Tutorial web site: 
http://www.disi.unige.it/person/MasulliF/conferences/IJCNN2009-tut.html

IJCNN 2009 web site: http://cnd.memphis.edu/ijcnn2009/

 
Abstract:
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 [1], many biclustering techniques have been 
proposed for bioinformatic data analysis [2]. 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 [7]) 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 [8] . 

References:
[1] Y. Cheng and G. M. Church, Biclustering of expression data. Proc Int Conf 
Intell Syst Mol Biol, vol. 8, pp. 93-103, 2000.
 [2] 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.
 [3] 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.
 [4] 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.
 [5] 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.
 [6] 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.
 [7] 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.
 [8] 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).


Speakers:
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 

Registration:
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


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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
and
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
url: http://www.disi.unige.it/person/MasulliF
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