- author = "Shital C. Shah and Andrew Kusiak",
- title = "Data mining and genetic algorithm based {gene/SNP} selection",
- journal = "Artificial Intelligence in Medicine",
- year = "2004",
- volume = "31",
- number = "3",
- pages = "183--196",
- month = jul,
- keywords = "genetic algorithms, genetic programming, Single nucleotide polymorphisms (SNPs), Genes, Feature selection, Data mining, Intersection approach, Drug effectiveness",
-
URL = "
http://www.icaen.uiowa.edu/~ankusiak/Journal-papers/Gen_Shital.pdf",
-
DOI = "
doi:10.1016/j.artmed.2004.04.002",
- size = "14 pages",
-
abstract = "Objective: Genomic studies provide large volumes of
data with the number of single nucleotide polymorphisms
(SNPs) ranging into thousands. The analysis of SNPs
permits determining relationships between genotypic and
phenotypic information as well as the identification of
SNPs related to a disease. The growing wealth of
information and advances in biology call for the
development of approaches for discovery of new
knowledge. One such area is the identification of
gene/SNP patterns impacting cure/drug development for
various diseases.
Methods: A new approach for predicting drug effectiveness is presented. The approach is based on data mining and genetic algorithms. A global search mechanism, weighted decision tree, decision-tree-based wrapper, a correlation-based heuristic, and the identification of intersecting feature sets are employed for selecting significant genes. Results: The feature selection approach has resulted in 85% reduction of number of features. The relative increase in cross-validation accuracy and specificity for the significant gene/SNP set was 10% and 3.2%, respectively. Conclusion: The feature selection approach was successfully applied to data sets for drug and placebo subjects. The number of features has been significantly reduced while the quality of knowledge was enhanced. The feature set intersection approach provided the most significant genes/SNPs. The results reported in the paper discuss associations among SNPs resulting in patient-specific treatment protocols.",
