A hybrid supervised approach to human population identification using genomics data Araghi, Sahar and Nguyen, Thanh Thi 2019, A hybrid supervised approach to human population identification using genomics data, IEEE/ACM transactions on computational biology and bioinformatics, pp. 1-12, doi: 10.1109/tcbb.2019.2919501. Attached Files Name Description MIMEType Size Downloads Title A hybrid supervised approach to human population identification using genomics data Author(s) Araghi, Sahar Nguyen, Thanh Thi orcid.org/0000-0001-9709-1663 Journal name IEEE/ACM transactions on computational biology and bioinformatics Start page 1 End page 12 Total pages 12 Publisher Institute of Electrical and Electronics Engineers Place of publication Piscataway, N.J. Publication date 2019-05-28 ISSN 1545-5963 2374-0043 Keyword(s) Population Structure Multinomial Classification PCA LASSO Personalised Treatment Notes In press Language eng DOI 10.1109/tcbb.2019.2919501 Indigenous content off Field of Research 010402 Biostatistics 010401 Applied Statistics 010202 Biological Mathematics 08 Information and Computing Sciences 06 Biological Sciences 01 Mathematical Sciences Socio Economic Objective 970101 Expanding Knowledge in the Mathematical Sciences HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2019, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30128489 Document type: Journal Article Collections: Centre for Intelligent Systems Research GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 31 Jul 2019, 16:39:05 EST Thu, 01 Aug 2019, 05:01:08 EST Thu, 01 Aug 2019, 12:14:29 EST Fri, 02 Aug 2019, 06:02:35 EST Fri, 02 Aug 2019, 13:35:50 EST Fri, 02 Aug 2019, 13:37:58 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 50 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Thu, 01 Aug 2019, 12:14:29 EST

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