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dc.contributor.authorBellgard, M.
dc.contributor.authorTaplin, Ross
dc.contributor.authorChapman, B.
dc.contributor.authorLivk, A.
dc.contributor.authorWellington, C.
dc.contributor.authorHunter, A.
dc.contributor.authorLipscombe, R.
dc.date.accessioned2017-01-30T11:50:55Z
dc.date.available2017-01-30T11:50:55Z
dc.date.created2014-02-27T20:00:41Z
dc.date.issued2013
dc.identifier.citationBellgard, Matthew and Taplin, Ross and Chapman, Brett and Livk, Andreja and Wellington, Crispin and Hunter, Adam and Lipscombe, Richard. 2013. Classification of Fish Samples via an Integrated Proteomics and Bioinformatics Approach. Proteomics. 13 (21): pp. 3124-3130.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/15635
dc.identifier.doi10.1002/pmic.201200426
dc.description.abstract

There is an increasing demand to develop cost-effective and accurate approaches to analyzing biological tissue samples. This is especially relevant in the fishing industry where closely related fish samples can be mislabeled, and the high market value of certain fish leads to the use of alternative species as substitutes, for example, Barramundi and Nile Perch (belonging to the same genus, Lates). There is a need to combine selective proteomic datasets with sophisticated computational analysis to devise a robust classification approach. This paper describes an integrated MS-based proteomics and bioinformatics approach to classifying a range of fish samples. A classifier is developed using training data that successfully discriminates between Barramundi and Nile Perch samples using a selected protein subset of the proteome. Additionally, the classifier is shown to successfully discriminate between test samples not used to develop the classifier, including samples that have been cooked, and to classify other fish species as neither Barramundi nor Nile Perch. This approach has applications to truth in labeling for fishmongers and restaurants, monitoring fish catches, and for scientific research into distances between species.

dc.publisherWiley - VCH Verlag GmbH & Co. KGaA
dc.subjectFish classification
dc.subjectNaïve Bayes classifier
dc.subjectBiomarkers
dc.subjectBioinformatics
dc.titleClassification of Fish Samples via an Integrated Proteomics and Bioinformatics Approach
dc.typeJournal Article
dcterms.source.volume13
dcterms.source.startPage3124
dcterms.source.endPage3130
dcterms.source.issn16159853
dcterms.source.titleProteomics
curtin.department
curtin.accessStatusFulltext not available


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