Data mining and genetic algorithm based gene/SNP selection.
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.


CONCLUSIONS
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.
Intelligent Systems Laboratory, MIE, 2139 Seamans Center, The University of Iowa, Iowa City, IA 52242-1527, USA.
Shital C
SC
Shah
Andrew
A
Kusiak
D000465:Algorithms; D003663:Decision Trees; D004358:Drug Therapy; D005796:Genes; D006801:Humans; D016247:Information Storage and Retrieval; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D011379:Prognosis; D016896:Treatment Outcome