C
Cheol Koo Lee
Researcher at Korea University
Publications - 59
Citations - 6202
Cheol Koo Lee is an academic researcher from Korea University. The author has contributed to research in topics: Gene expression & Gene. The author has an hindex of 22, co-authored 57 publications receiving 5955 citations. Previous affiliations of Cheol Koo Lee include University of North Carolina at Chapel Hill & Seoul National University.
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Gene Expression Profile of Aging and Its Retardation by Caloric Restriction
TL;DR: Transcriptional patterns of calorie-restricted animals suggest that caloric restriction retards the aging process by causing a metabolic shift toward increased protein turnover and decreased macromolecular damage.
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Gene-expression profile of the ageing brain in mice.
TL;DR: Caloric restriction, which retards the ageing process in mammals, selectively attenuated the age-associated induction of genes encoding inflammatory and stress responses, which resulted in a gene-expression profile indicative of an inflammatory response, oxidative stress and reduced neurotrophic support in both brain regions.
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Evidence for nucleosome depletion at active regulatory regions genome-wide.
TL;DR: Evidence is presented that the basic repeating units of eukaryotic chromatin, nucleosomes, are depleted from active regulatory elements throughout the Saccharomyces cerevisiae genome in vivo and the level of nucleosome occupancy is inversely proportional to the transcriptional initiation rate at the promoter.
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A mixture model approach for the analysis of microarray gene expression data
David B. Allison,Gary L. Gadbury,Moonseong Heo,Jose R. Fernandez,Cheol Koo Lee,Thomas A. Prolla,Richard Weindruch +6 more
TL;DR: A sequence of procedures involving finite mixture modeling and bootstrap inference is developed to address issues in studies involving many thousands of genes, including calorically restricted mice.
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Classification of multiple cancer types by multicategory support vector machines using gene expression data.
Yoonkyung Lee,Cheol Koo Lee +1 more
TL;DR: The Multicategory SVM is introduced, which is a recently proposed extension of the binary SVM, and applied to multiclass cancer diagnosis problems, which renders the MSVM a viable alternative to other classification methods.