Grant Daggard

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We investigate the idea of using diversified multiple trees for Microarray data classification. We propose an algorithm of Maximally Diversified Multiple Trees (MDMT), which makes use of a set of unique trees in the decision committee. We compare MDMT with some well-known ensemble methods, namely Ad-aBoost, Bagging, and Random Forests. We also compare MDMT(More)
In response to the rapid development of DNA Mi-croarray technology, many classification methods have been used for Microarray classification. SVMs, decision trees, Bagging, Boosting and Random Forest are commonly used methods. In this paper, we conduct experimental comparison of LibSVMs, C4.5, BaggingC4.5, AdaBoostingC4.5, and Random Forest on seven(More)
In recent years, the rapid development of DNA Microarray technology has made it possible for scientists to monitor the expression level of thousands of genes in a single experiment. As a new technology , Microarray data presents some fresh challenges to scientists since Microarray data contains a large number of genes (around tens thousands) with a small(More)
The immunogenicity and protective efficacy of a DNA vaccine encoding a genetically inactivated S1 domain of pertussis toxin was evaluated using a murine respiratory challenge model of Bordetella pertussis infection. It was found that mice immunized via the intramuscular route elicited a purely cell-mediated immune response to the DNA vaccine, with high(More)
In this paper we propose a clustering algorithm called s-Cluster for analysis of gene expression data based on pattern-similarity. The algorithm captures the tight clusters exhibiting strong similar expression patterns in Microarray data,and allows a high level of overlap among discovered clusters without completely grouping all genes like other algorithms.(More)
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