EGEA : A New Hybrid Approach Towards Extracting Reduced Generic Association Rule Set (Application to AML Blood Cancer Therapy)
To avoid obtaining an unmanageable highly sized association rule sets– compounded with their low precision– that often make the perusal of knowledge ineffective, the extraction and exploitation of compact and informative generic basis of association rules is a becoming a must. Moreover, they provide a powerful verification technique for hampering gene mis-annotating or badly clustering in the Unigene library. However, extracted generic basis is still oversized and their exploitation is impractical. Thus, providing critical nuggets of extra-valued knowledge is a compellingly addressable issue. To tackle such a drawback, we propose in this paper a novel approach, called EGEA (Evolutionary Gene Extraction Approach). Such approach aims to considerably reduce the quantity of knowledge, extracted from a gene expression dataset, presented to an expert. Thus, we use a genetic algorithm to select the more predictive set of genes related to patient situations. Once, the relevant attributes (genes) have been selected, they serve as an input for a second approach stage, i.e., extracting generic association rules from this reduced set of genes. The notably decrease of the generic association rule cardinality, extracted from the selected gene set, permits to improve the quality of knowledge exploitation. Carried out experiments on a benchmark dataset pointed out that among this set, there are genes which are previously unknown prognosis-associated genes. This may serve as molecular targets for new therapeutic strategies to repress the relapse of pediatric acute myeloid leukemia (AML).
KeywordsGeneric association rules Genetic Algorithms Neural networks Frequent Closed itemset algorithms Bioinformatics
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