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Mask Functions for the Symbolic Modeling of Epistasis Using Genetic Programming
Ryan J Urbanowicz1, Bill C White, Jason H Moore
1Dartmouth College, 1 Medical Center Dr., Hanover, NH 03755, USA.
Genetic and Evolutionary Computation Conference : [Proceedings]. Genetic and Evolutionary Computation Conference
|September 29, 2012
Summary
Introducing a genetic "mask" improves symbolic discriminant analysis (SDA) performance in genetic epidemiology. Pre-processing data with this novel building block enhances genetic programming (GP) models for complex multifactorial diseases.
Area of Science:
- Genetic Epidemiology
- Computational Biology
- Bioinformatics
Background:
- Complex multifactorial diseases present challenges in genetic epidemiology due to nonlinear genotype-to-phenotype relationships.
- Epistasis, or gene-gene interactions, significantly complicates the mapping from genotype to phenotype.
Purpose of the Study:
- To introduce and evaluate the effectiveness of a novel building block, the genetic "mask", within Symbolic Discriminant Analysis (SDA).
- To determine if incorporating expert knowledge via "mask" building blocks enhances SDA performance for modeling gene-gene interactions.
Main Methods:
- Symbolic Discriminant Analysis (SDA) utilizing Genetic Programming (GP) to evolve predictive models.
- Introduction of the "genetic mask" as a new building block, encoding pre-constructed relationships between attributes.
- Comparison of SDA performance with and without the "genetic mask" building blocks.
Main Results:
- The "genetic mask" building block was successfully integrated into the SDA framework.
- The results indicate that the availability of "mask" building blocks improves SDA performance.
- Pre-processing data with expert-derived relationships enhances Genetic Programming (GP) model efficacy.
Conclusions:
- The "genetic mask" is a valuable addition to SDA, improving its ability to model complex genetic interactions.
- Data pre-processing, particularly incorporating expert knowledge, is a beneficial strategy for enhancing GP performance in genetic epidemiology.
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