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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-0df0bef3-59eb-4b0a-af40-f46efc7c8e17

Czasopismo

Biocybernetics and Biomedical Engineering

Tytuł artykułu

Improving the accuracy of detecting steroid abuse in cattle by pairwise learning of serum samples

Autorzy Xia, X. L. 
Treść / Zawartość http://www.ibib.waw.pl/pl/wydawnictwa/biocybernetics-and-biomedical-enginering-bbe/bbe-tomy http://www.journals.elsevier.com/biocybernetics-and-biomedical-engineering/
Warianty tytułu
Języki publikacji EN
Abstrakty
EN Issues surrounding the misuse of illegal drugs in animals destined for food production have be an enormous challenge to regulatory authorities charged with enforcing their control. A method has been proposed recently which compared the bovine blood biochemistry profiles between control and treated animals, using the support vector machine (SVM) as the classification tool. Whether an animal has been treated is determined by the classification outcome of the SVM on an individual serum sample taken off the animal. However, the acquisition time of the serum sample is essential in the classification performance of the SVM. Thus, the paper proposed to collect and analyze a pair of samples, in order to obtain at least one sample whose acquisition time resulted in an SVM with the highest sensitivity. The power of the strategy in improving sensitivity was theoretically proven to be up to 0.25 and empirically confirmed on a bovine blood biochemistry data. Furthermore, classification rules of the SVM were proposed to be adapted to meet higher levels of demands on sensitivity. Schemes were described which optimized the time apart between the collection of the two samples and the impact of the proposed strategy on specificity was also investigated.
Słowa kluczowe
PL maszyna wektorów wspierających   nadużywanie steroidów   metoda przesiewowa  
EN support vector machine   steroid abuse   screening method  
Wydawca Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
Elsevier
Czasopismo Biocybernetics and Biomedical Engineering
Rocznik 2017
Tom Vol. 37, no. 3
Strony 510--519
Opis fizyczny Bibliogr. 20 poz., tab., wykr.
Twórcy
autor Xia, X. L.
  • School of Mechanical and Electrical Engineering, Jiaxing University, Jiaxing 314001, PR China, xxia01@qub.ac.uk
Bibliografia
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[20] Xia X-L, Xing H, Liu X. Analyzing kernel matrices for the identification of differentially expressed genes. PLoS ONE 2013;8(12):e81683.
Uwagi
PL Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-0df0bef3-59eb-4b0a-af40-f46efc7c8e17
Identyfikatory
DOI 10.1016/j.bbe.2017.05.009