Development and Evaluation of an Open-Ended Computational Evolution System for the Genetic Analysis of Susceptibility to Common Human Diseases
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{conf/evoW/MooreABW08,
-
title = "Development and Evaluation of an Open-Ended
Computational Evolution System for the Genetic Analysis
of Susceptibility to Common Human Diseases",
-
author = "Jason H. Moore and Peter C. Andrews and
Nate Barney and Bill C. White",
-
bibdate = "2008-04-15",
-
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/evoW/evobio2008.html#MooreABW08",
-
booktitle = "Proceedings of the 6th European Conference, on
Evolutionary Computation, Machine Learning and Data
Mining in Bioinformatics, Evo{BIO} 2008",
-
publisher = "Springer",
-
year = "2008",
-
volume = "4973",
-
editor = "Elena Marchiori and Jason H. Moore",
-
isbn13 = "978-3-540-78756-3",
-
pages = "129--140",
-
series = "Lecture Notes in Computer Science",
-
DOI = "doi:10.1007/978-3-540-78757-0_12",
-
address = "Naples, Italy",
-
month = mar # " 26-28",
-
keywords = "genetic algorithms, genetic programming, computational
evolution",
-
abstract = "An important goal of human genetics is to identify DNA
sequence variations that are predictive of
susceptibility to common human diseases. This is a
classification problem with data consisting of discrete
attributes and a binary outcome. A variety of different
machine learning methods based on artificial evolution
have been developed and applied to modelling the
relationship between genotype and phenotype. While
artificial evolution approaches show promise, they are
far from perfect and are only loosely based on real
biological and evolutionary processes. It has recently
been suggested that a new paradigm is needed where
artificial evolution is transformed to computational
evolution (CE) by incorporating more biological and
evolutionary complexity into existing algorithms. It
has been proposed that CE systems will be more likely
to solve problems of interest to biologists and
biomedical researchers. The goal of the present study
was to develop and evaluate a prototype CE system for
the analysis of human genetics data. We describe here
this new open-ended CE system and provide initial
results from a simulation study that suggests more
complex operators result in better solutions.",
- }
Genetic Programming entries for
Jason H Moore
Peter C Andrews
Nate Barney
Bill C White
Citations