TY - GEN
T1 - Incorporating domain knowledge into evolutionary computing for discovering gene-gene interaction
AU - Turner, Stephen D.
AU - Dudek, Scott M.
AU - Ritchie, Marylyn D.
PY - 2010/11/12
Y1 - 2010/11/12
N2 - Understanding the genetic underpinnings of common heritable human traits has enormous public health benefits with implications for risk prediction, development of novel drugs, and personalized medicine. Many complex human traits are highly heritable, yet little of the variability in such traits can be accounted for by examining single DNA variants at a time. Seldom explored non-additive gene-gene interactions are thought to be one source of this "missing" heritability. Approaches that can account for this complexity are more aptly suited to find combinations of genetic and environmental exposures that can lead to disease. Stochastic methods employing evolutionary algorithms have demonstrated promise in being able to detect and model gene-gene interactions that influence human traits, yet the search space is nearly infinite because of the vast number of variables collected in contemporary human genetics studies. In this work we assess the performance and feasibility of sensible initialization of an evolutionary algorithm using domain knowledge.
AB - Understanding the genetic underpinnings of common heritable human traits has enormous public health benefits with implications for risk prediction, development of novel drugs, and personalized medicine. Many complex human traits are highly heritable, yet little of the variability in such traits can be accounted for by examining single DNA variants at a time. Seldom explored non-additive gene-gene interactions are thought to be one source of this "missing" heritability. Approaches that can account for this complexity are more aptly suited to find combinations of genetic and environmental exposures that can lead to disease. Stochastic methods employing evolutionary algorithms have demonstrated promise in being able to detect and model gene-gene interactions that influence human traits, yet the search space is nearly infinite because of the vast number of variables collected in contemporary human genetics studies. In this work we assess the performance and feasibility of sensible initialization of an evolutionary algorithm using domain knowledge.
UR - http://www.scopus.com/inward/record.url?scp=78149277633&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-15844-5_40
DO - 10.1007/978-3-642-15844-5_40
M3 - Conference contribution
AN - SCOPUS:78149277633
SN - 3642158439
SN - 9783642158438
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 394
EP - 403
BT - Parallel Problem Solving from Nature, PPSN XI - 11th International Conference, Proceedings
T2 - 11th International Conference on Parallel Problem Solving from Nature, PPSN 2010
Y2 - 11 September 2010 through 15 September 2010
ER -
