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A Parallel Classification Method for Genomic and Proteomic Problems
Vienna, Austria April 18-April 20
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/AINA.2006.4720th International Conference on Adva ...
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M. R. Guarracino, C. Cifarelli, O. Seref, P. M. Pardalos, "A Parallel Classification Method for Genomic and Proteomic Problems," Advanced Information Networking and Applications, International Conference on, vol. 2, pp. 588-592, 20th International Conference on Advanced Information Networking and Applications - Volume 2 (AINA'06), 2006.
 
 
BibTex
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@article{ 10.1109/AINA.2006.47,
author = {M. R. Guarracino and C. Cifarelli and O. Seref and P. M. Pardalos},
title = {A Parallel Classification Method for Genomic and Proteomic Problems},
journal ={Advanced Information Networking and Applications, International Conference on},
volume = {2},
year = {2006},
issn = {1550-445X},
pages = {588-592},
doi = {http://doi.ieeecomputersociety.org/10.1109/AINA.2006.47},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Advanced Information Networking and Applications, International Conference on
TI - A Parallel Classification Method for Genomic and Proteomic Problems
SN - 1550-445X
SP588
EP592
A1 - M. R. Guarracino,
A1 - C. Cifarelli,
A1 - O. Seref,
A1 - P. M. Pardalos,
PY - 2006
KW - null
VL - 2
JA - Advanced Information Networking and Applications, International Conference on
ER -
 
M. R. Guarracino, High Performance Computing and Networking Institute,National Research Council, Italy
C. Cifarelli, University of Rome "La Sapienza", Italy
O. Seref, University of Florida, Gainesville, FL
P. M. Pardalos, University of Florida, Gainesville, FL
Classification is one of the most widely used method in data mining with numerous applications in biomedicine. The scope and the resolution of data involved in many real life applications require very efficient implementations of classification methods, developed to run on parallel or distributed computational systems. In this study, a parallel implementation of an efficient algorithm that is based on regularized general eigenvalue classification is introduced. The proposed implementation is tested on a very large scale genomic data base and preliminary results regarding efficiency are presented.
Citation:
M. R. Guarracino, C. Cifarelli, O. Seref, P. M. Pardalos, "A Parallel Classification Method for Genomic and Proteomic Problems," aina, vol. 2, pp.588-592, 20th International Conference on Advanced Information Networking and Applications - Volume 2 (AINA'06), 2006
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