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Machine-learning prediction of cancer survival: a prospective study examining the impact of combining clinical and genomic data

Ashley, David M., Gupta, Sunil, Tran, Truyen, Luo, Wei, Lorgelly, Paula K., Thomas, David Morgan, Fox, Stephen B. and Venkatesh, Svetha 2015, Machine-learning prediction of cancer survival: a prospective study examining the impact of combining clinical and genomic data, in ASCO 2015: Proceedings of the 2015 Annual Meeting of American Society of Clinical Oncology, American Society of Clinical Oncology, Alexandria, Va..

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Title Machine-learning prediction of cancer survival: a prospective study examining the impact of combining clinical and genomic data
Author(s) Ashley, David M.
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Luo, WeiORCID iD for Luo, Wei orcid.org/0000-0002-4711-7543
Lorgelly, Paula K.
Thomas, David Morgan
Fox, Stephen B.
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name ASCO Annual Meeting (2015: Chicago, Ill.)
Conference location Chicago, Ill.
Conference dates 29 May - 2 Jun. 2015
Title of proceedings ASCO 2015: Proceedings of the 2015 Annual Meeting of American Society of Clinical Oncology
Publication date 2015
Series Journal of Clinical Oncology, 2015 ASCO Annual Meeting (May 29 - June 2, 2015)
Total pages 1
Publisher American Society of Clinical Oncology
Place of publication Alexandria, Va.
Keyword(s) Science & Technology
Life Sciences & Biomedicine
Oncology
Notes Abstract 6521, not full paper. Published in a supplement to the journal.
ISSN 0732-183X
1527-7755
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
1112 Oncology And Carcinogenesis
111299 Oncology and Carcinogenesis not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E3 Extract of paper
ERA Research output type X Not reportable
Copyright notice ©2015, American Society of Clinical Oncology
Persistent URL http://hdl.handle.net/10536/DRO/DU:30077467

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