Computational intelligence techniques: a study of
scleroderma skin disease
J. Valdes, and A. Barton. Late breaking paper at Genetic and Evolutionary
Computation Conference (GECCO'2007), page 2580--2587. London, United Kingdom, ACM Press, (7-11 July 2007)
Abstract
This paper presents an analysis of microarray gene
expression data from patients with and without
scleroderma skin disease using computational
intelligence and visual data mining techniques. Virtual
reality spaces are used for providing unsupervised
insight about the information content of the original
set of genes describing the objects. These spaces are
constructed by hybrid optimization algorithms based on
a combination of Differential Evolution (DE) and
Particle Swarm Optimization respectively, with
deterministic Fletcher-Reeves optimisation. A
distributed-pipelined data mining algorithm composed of
clustering and cross-validated rough sets analysis is
applied in order to find subsets of relevant attributes
with high classification capabilities. Finally, genetic
programming (GP) is applied in order to find explicit
analytic expressions for the characteristic functions
of the scleroderma and the normal classes. The virtual
reality spaces associated with the set of function
arguments (genes) are also computed. Several small
subsets of genes are discovered which are capable of
classifying the data with complete accuracy. They
represent genes potentially relevant to the
understanding of the scleroderma disease.
%0 Conference Paper
%1 1274028
%A Valdes, Julio J.
%A Barton, Alan J.
%B Late breaking paper at Genetic and Evolutionary
Computation Conference (GECCO'2007)
%C London, United Kingdom
%D 2007
%E Bosman, Peter A. N.
%I ACM Press
%K Optimisation, Particle Swarm algorithms, computing, data differential disease, evolution, evolutionary-classical genetic genomics, grid hybrid mining optimisation, preservation, programming, reality, rough scleroderma sets, similarity structure virtual visual
%P 2580--2587
%T Computational intelligence techniques: a study of
scleroderma skin disease
%U http://doi.acm.org/10.1145/1274000.1274028
%X This paper presents an analysis of microarray gene
expression data from patients with and without
scleroderma skin disease using computational
intelligence and visual data mining techniques. Virtual
reality spaces are used for providing unsupervised
insight about the information content of the original
set of genes describing the objects. These spaces are
constructed by hybrid optimization algorithms based on
a combination of Differential Evolution (DE) and
Particle Swarm Optimization respectively, with
deterministic Fletcher-Reeves optimisation. A
distributed-pipelined data mining algorithm composed of
clustering and cross-validated rough sets analysis is
applied in order to find subsets of relevant attributes
with high classification capabilities. Finally, genetic
programming (GP) is applied in order to find explicit
analytic expressions for the characteristic functions
of the scleroderma and the normal classes. The virtual
reality spaces associated with the set of function
arguments (genes) are also computed. Several small
subsets of genes are discovered which are capable of
classifying the data with complete accuracy. They
represent genes potentially relevant to the
understanding of the scleroderma disease.
@inproceedings{1274028,
abstract = {This paper presents an analysis of microarray gene
expression data from patients with and without
scleroderma skin disease using computational
intelligence and visual data mining techniques. Virtual
reality spaces are used for providing unsupervised
insight about the information content of the original
set of genes describing the objects. These spaces are
constructed by hybrid optimization algorithms based on
a combination of Differential Evolution (DE) and
Particle Swarm Optimization respectively, with
deterministic Fletcher-Reeves optimisation. A
distributed-pipelined data mining algorithm composed of
clustering and cross-validated rough sets analysis is
applied in order to find subsets of relevant attributes
with high classification capabilities. Finally, genetic
programming (GP) is applied in order to find explicit
analytic expressions for the characteristic functions
of the scleroderma and the normal classes. The virtual
reality spaces associated with the set of function
arguments (genes) are also computed. Several small
subsets of genes are discovered which are capable of
classifying the data with complete accuracy. They
represent genes potentially relevant to the
understanding of the scleroderma disease.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {London, United Kingdom},
author = {Valdes, Julio J. and Barton, Alan J.},
biburl = {https://www.bibsonomy.org/bibtex/2727ca39be34efa03d9ee011b285fc8ce/brazovayeye},
booktitle = {Late breaking paper at Genetic and Evolutionary
Computation Conference {(GECCO'2007)}},
editor = {Bosman, Peter A. N.},
interhash = {1fa894079ea36da4a9a78a1a7bf968e9},
intrahash = {727ca39be34efa03d9ee011b285fc8ce},
isbn13 = {978-1-59593-698-1},
keywords = {Optimisation, Particle Swarm algorithms, computing, data differential disease, evolution, evolutionary-classical genetic genomics, grid hybrid mining optimisation, preservation, programming, reality, rough scleroderma sets, similarity structure virtual visual},
month = {7-11 July},
notes = {Distributed on CD-ROM at GECCO-2007 ACM Order No.
910071},
pages = {2580--2587},
publisher = {ACM Press},
publisher_address = {New York, NY, USA},
timestamp = {2008-06-19T17:53:27.000+0200},
title = {Computational intelligence techniques: a study of
scleroderma skin disease},
url = {http://doi.acm.org/10.1145/1274000.1274028},
year = 2007
}