On Genetic Programming and Knowledge Discovery in
Transcriptome Data
J. Rowland. Proceedings of the 2004 IEEE Congress on Evolutionary
Computation, page 158--165. Portland, Oregon, IEEE Press, (20-23 June 2004)
Abstract
This paper concerns the use of Genetic Programming
(GP) for supervised classification of transcriptome
(gene expression) data. In such applications GP can
produce accurate predictive models that generalize well
and use only very few gene expression values. It is
often suggested that the selected genes are therefore
of biological significance in discriminating the
classes. The paper presents a preliminary study of
successful parsimonious GP models to investigate the
extent to which the selected variables contribute to
the classification. The work is based on a readily
available and well studied dataset that represents gene
expression values for two groups of patients with
different forms of Leukaemia.
%0 Conference Paper
%1 rowland:2004:ogpakditd
%A Rowland, Jem
%B Proceedings of the 2004 IEEE Congress on Evolutionary
Computation
%C Portland, Oregon
%D 2004
%I IEEE Press
%K Bioinformatics Biology Computation Computational Evolutionary algorithms, and genetic in programming,
%P 158--165
%T On Genetic Programming and Knowledge Discovery in
Transcriptome Data
%X This paper concerns the use of Genetic Programming
(GP) for supervised classification of transcriptome
(gene expression) data. In such applications GP can
produce accurate predictive models that generalize well
and use only very few gene expression values. It is
often suggested that the selected genes are therefore
of biological significance in discriminating the
classes. The paper presents a preliminary study of
successful parsimonious GP models to investigate the
extent to which the selected variables contribute to
the classification. The work is based on a readily
available and well studied dataset that represents gene
expression values for two groups of patients with
different forms of Leukaemia.
%@ 0-7803-8515-2
@inproceedings{rowland:2004:ogpakditd,
abstract = {This paper concerns the use of Genetic Programming
(GP) for supervised classification of transcriptome
(gene expression) data. In such applications GP can
produce accurate predictive models that generalize well
and use only very few gene expression values. It is
often suggested that the selected genes are therefore
of biological significance in discriminating the
classes. The paper presents a preliminary study of
successful parsimonious GP models to investigate the
extent to which the selected variables contribute to
the classification. The work is based on a readily
available and well studied dataset that represents gene
expression values for two groups of patients with
different forms of Leukaemia.},
added-at = {2008-06-19T17:46:40.000+0200},
address = {Portland, Oregon},
author = {Rowland, Jem},
biburl = {https://www.bibsonomy.org/bibtex/27d52d9454ee28d82eb1fe876b70a545d/brazovayeye},
booktitle = {Proceedings of the 2004 IEEE Congress on Evolutionary
Computation},
interhash = {a0f50f975699ffb82d5cb1f5444bc6b3},
intrahash = {7d52d9454ee28d82eb1fe876b70a545d},
isbn = {0-7803-8515-2},
keywords = {Bioinformatics Biology Computation Computational Evolutionary algorithms, and genetic in programming,},
month = {20-23 June},
notes = {CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.},
pages = {158--165},
publisher = {IEEE Press},
timestamp = {2008-06-19T17:50:46.000+0200},
title = {On Genetic Programming and Knowledge Discovery in
Transcriptome Data},
year = 2004
}