Title
ATHENA optimization: the effect of initial parameter settings across different genetic models
Abstract
Rapidly advancing technology has allowed for the generation of massive amounts data assessing variation across the human genome. One analysis method for this type of data is the genome-wide association study (GWAS) where each variation is assessed individually for association to disease. While these studies have elucidated novel etiology, much of the variation due to genetics remains unexplained. One hypothesis is that some of the variation lies in gene-gene interactions. An impediment to testing for interactions is the infeasibility of exhaustively searching all multi-locus models. Novel methods are being developed that perform a non-exhaustive search. Because these methods are new to genetic studies, rigorous parameter optimization is necessary. Here, we assess genotype encodings, function sets, and cross-over in two algorithms which use grammatical evolution to optimize neural networks or symbolic regression formulas in the ATHENA software package. Our results show that the effect of these parameters is highly dependent on the underlying disease model.
Year
DOI
Venue
2011
10.1007/978-3-642-20389-3_5
EvoBIO
Keywords
Field
DocType
genetic study,novel method,genome-wide association study,athena software package,initial parameter setting,analysis method,underlying disease model,different genetic model,function set,massive amounts data,gene-gene interaction,athena optimization,elucidated novel etiology,human genome,exhaustive search,genetics,genome wide association study,neural network,grammatical evolution
Computer science,Genome-wide association study,Software,Artificial intelligence,Bioinformatics,Artificial neural network,Grammatical evolution,Symbolic regression,Machine learning
Conference
Volume
ISSN
Citations 
6623
0302-9743
4
PageRank 
References 
Authors
0.63
9
4
Name
Order
Citations
PageRank
Emily Rose Holzinger1334.50
Scott M. Dudek220626.27
Eric C. Torstenson391.20
Marylyn D. Ritchie469286.79