Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Yoonhee Kim"'
Autor:
Heejong Sung1, Yoonhee Kim1, Juanliang Cai1, Cropp, Cheryl D.2, Simpson, Claire L.2, Qing Li2, Perry, Brian C.2, Sorant, Alexa J. M.1, Bailey-Wilson, Joan E.2, Wilson, Alexander F.1 afw@mail.nih.gov
Publikováno v:
BMC Proceedings. 2011 Supplement 9, Vol. 5 Issue Suppl 9, p1-5. 5p. 2 Charts, 2 Graphs.
Autor:
Yoonhee Kim1, Qing Li1, Cropp, Cheryl D.1, Heejong Sung1, Juanliang Cai1, Simpson, Claire L.1, Perry, Brian1, Dasgupta, Abhijit2, Malley, James D.3, Wilson, Alexander F.1, Bailey-Wilson, Joan E.1 jebw@mail.nih.gov
Publikováno v:
BMC Proceedings. 2011 Supplement 9, Vol. 5 Issue Suppl 9, p1-6. 6p. 1 Chart, 1 Graph.
Autor:
Yoonhee Kim1 kimyoo@mail.nih.gov, Wojciechowski, Robert1 robwoj@mail.nih.gov, Heejong Sung1 sunghe@mail.nih.gov, Mathias, Rasika A.1 rmathias1@mail.nih.gov, Li Wang1,2 liwang@jhsph.edu, Klein, Alison P.1,2 aklein1@jhmi.edu, Lenroot, Rhoshel K.3 lenrootr@mail.nih.gov, Bailey-Wilson, Joan E.1 jebw@mail.nih.gov, Malley, James4 jmalley@mail.nih.gov
Publikováno v:
BMC Proceedings. 2009 Supplement 7, Vol. 3, Special section p1-6. 6p. 2 Charts.
Autor:
Yoonhee Kim, Juanliang Cai, Heejong Sung, Qing Li, Brian C Perry, Joan E. Bailey-Wilson, Alexander F. Wilson, Claire L. Simpson, Alexa J.M. Sorant, Cheryl D. Cropp
Publikováno v:
BMC Proceedings
Tiled regression is an approach designed to determine the set of independent genetic variants that contribute to the variation of a quantitative trait in the presence of many highly correlated variants. In this study, we evaluate the statistical prop
Autor:
James D. Malley, Brian C Perry, Qing Li, Joan E. Bailey-Wilson, Abhijit Dasgupta, Cheryl D. Cropp, Yoonhee Kim, Claire L. Simpson, Heejong Sung, Juanliang Cai, Alexander F. Wilson
Publikováno v:
BMC Proceedings, Vol 5, Iss Suppl 9, p S104 (2011)
BMC Proceedings
BMC Proceedings
Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learni
Autor:
Li Wang, James D. Malley, Rasika A. Mathias, Heejong Sung, Joan E. Bailey-Wilson, Yoonhee Kim, Rhoshel K. Lenroot, Alison P. Klein, Robert Wojciechowski
Publikováno v:
BMC Proceedings
Random forests (RF) is one of a broad class of machine learning methods that are able to deal with large-scale data without model specification, which makes it an attractive method for genome-wide association studies (GWAS). The performance of RF and
Publikováno v:
BMC Proceedings, Vol 1, Iss Suppl 1, p S152 (2007)
BMC Proceedings
BMC Proceedings
"Genetical genomics", the study of natural genetic variation combining data from genetic marker-based studies with gene expression analyses, has exploded with the recent development of advanced microarray technologies. To account for systematic varia