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MIB Lab @ Queen's University's Projects

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An implementation and interactive interface for evolutionary algorithms as credit for the Advanced Undergraduate Project at Queen's Univeristy

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A combination of the multi-objective genetic algorithm for feature selection and permutation based feature importance as a framework for discovering higher-order feature interactions in predictive models, submitted to the Artificial Intelligence, Ethics, and Society Conference (AIES 2022)

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Interactive result visualization website for LGP algorithm

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Designed for high-dimensional GWAS dataset, network based subject portrait approach together with the accompanying feature transformation method determines the collective risk impact of multiple genetic interactions for each subject. The resulting transformed features can improve the performance of the predictive analysis in GWAS.

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Linear Genetic Programming Implemented in python with sklearn compatible API

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