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Sensible Initialization Using Expert Knowledge for Genome-Wide Analysis of Epistasis Using Genetic Programming
Casey S Greene1, Bill C White, Jason H Moore
1Dartmouth College, Lebanon, NH. USA.
Genetic and Evolutionary Computation Conference : [Proceedings]. Genetic and Evolutionary Computation Conference
|January 4, 2011
Summary
This study introduces an expert knowledge aware initializer for genetic programming to predict common human diseases. This novel approach effectively identifies predictive DNA sequence variations, outperforming random and enumerative methods.
Area of Science:
- Human genetics
- Computational biology
- Machine learning
Background:
- Large-scale DNA sequence variation data is now available.
- Disease risk is likely influenced by interactions between biological components, including DNA variations.
- Identifying predictive combinations of genetic variations is a key machine learning challenge.
Purpose of the Study:
- To evaluate the effectiveness of an expert knowledge aware initializer within a genetic programming framework.
- To improve the identification of DNA sequence variations predictive of common human diseases.
Main Methods:
- Utilizing genetic programming to explore interactions in large DNA sequence variation datasets.
- Comparing an expert knowledge aware initializer against random and enumerative initializers.
Main Results:
- The expert knowledge aware initializer demonstrated superior performance compared to random and enumerative initializers.
- Successfully identified combinations of DNA sequence variations predictive of common human diseases.
Conclusions:
- Expert knowledge integration in genetic programming enhances the prediction of disease risk from genetic variations.
- This approach offers a promising avenue for understanding complex genetic contributions to common diseases.
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