Infection status outcome, machine learning method and virus type interact to affect the optimised prediction of hepatitis virus immunoassay results from routine pathology laboratory assays in unbalanced data.
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%0 Journal Article
%1 journals/bmcbi/RichardsonL13
%A Richardson, Alice M.
%A Lidbury, Brett A.
%D 2013
%J BMC Bioinform.
%K dblp
%P 206
%T Infection status outcome, machine learning method and virus type interact to affect the optimised prediction of hepatitis virus immunoassay results from routine pathology laboratory assays in unbalanced data.
%U http://dblp.uni-trier.de/db/journals/bmcbi/bmcbi14.html#RichardsonL13
%V 14
@article{journals/bmcbi/RichardsonL13,
added-at = {2020-03-15T00:00:00.000+0100},
author = {Richardson, Alice M. and Lidbury, Brett A.},
biburl = {https://www.bibsonomy.org/bibtex/2c021d97f4a9f0a6967f1e6b357898d24/dblp},
ee = {https://www.wikidata.org/entity/Q30651603},
interhash = {1e3ab177cefe14a2b8921b3131f70406},
intrahash = {c021d97f4a9f0a6967f1e6b357898d24},
journal = {BMC Bioinform.},
keywords = {dblp},
pages = 206,
timestamp = {2020-03-17T11:50:24.000+0100},
title = {Infection status outcome, machine learning method and virus type interact to affect the optimised prediction of hepatitis virus immunoassay results from routine pathology laboratory assays in unbalanced data.},
url = {http://dblp.uni-trier.de/db/journals/bmcbi/bmcbi14.html#RichardsonL13},
volume = 14,
year = 2013
}
