TY - GEN
T1 - Microarray dimension reduction based on maximizing Mantel correlation coefficients using a genetic algorithm search strategy
AU - Deych, Elena
AU - Culverhouse, Robert
AU - Shannon, William D.
PY - 2010
Y1 - 2010
N2 - We present the GA-Mantel algorithm to find in high dimensional microarray data a subset of genes that captures relevant spatial relationships among the samples, in order to reduce the data for further analysis and eventually to identify meaningful biological markers. GA-Mantel uses a genetic algorithm to search over possible probe subsets using the Mantel correlation as the scoring measure for assessing the quality of any given probe subset, and consensus methods for selecting the final list of important genes. GA-Mantel is evaluated on both artificial data sets and on experimental microarray data taken from leukemia patients. Current results indicate the GA-Mantel method exhibits promise as a way of efficiently identifying information-rich gene subsets in large data sets while avoiding the curse of dimensionality.
AB - We present the GA-Mantel algorithm to find in high dimensional microarray data a subset of genes that captures relevant spatial relationships among the samples, in order to reduce the data for further analysis and eventually to identify meaningful biological markers. GA-Mantel uses a genetic algorithm to search over possible probe subsets using the Mantel correlation as the scoring measure for assessing the quality of any given probe subset, and consensus methods for selecting the final list of important genes. GA-Mantel is evaluated on both artificial data sets and on experimental microarray data taken from leukemia patients. Current results indicate the GA-Mantel method exhibits promise as a way of efficiently identifying information-rich gene subsets in large data sets while avoiding the curse of dimensionality.
UR - https://www.scopus.com/pages/publications/84879566761
U2 - 10.1007/978-3-642-10745-0_5
DO - 10.1007/978-3-642-10745-0_5
M3 - Conference contribution
AN - SCOPUS:84879566761
SN - 9783642107443
T3 - Studies in Classification, Data Analysis, and Knowledge Organization
SP - 49
EP - 60
BT - Classification as a Tool for Research - Proceedings of the 11th IFCS Biennial Conference and 33rd Annual Conference of the Gesellschaft fur Klassifikation e.V., GfKl 2009
PB - Kluwer Academic Publishers
T2 - 11th Biennial Conference of the International Federation of Classification Societies, IFCS 2009 and with the 33rd Annual Conf of the German Classification Society (Gesellschaft fur Klassifikation) on Classification as a Tool fo Research, GfKl 2009
Y2 - 13 March 2009 through 18 March 2009
ER -