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Short communication: Use of genomic and metabolic information as well as milk performance records for prediction of subclinical ketosis risk via artificial neural networks

Ehret, A., Hochstuhl, D., Krattenmacher, N., Tetens, J., Klein, M. S., Gronwald, Wolfram and Thaller, G. (2015) Short communication: Use of genomic and metabolic information as well as milk performance records for prediction of subclinical ketosis risk via artificial neural networks. Journal of dairy science 98 (1), pp. 322-329.

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Abstract

Subclinical ketosis is one of the most prevalent metabolic disorders in high-producing dairy cows during early lactation. This renders its early detection and prevention important for both economical and animal-welfare reasons. Construction of reliable predictive models is challenging, because traits like ketosis are commonly affected by multiple factors. In this context, machine learning methods ...

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Item type:Article
Date:January 2015
Institutions:Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Funktionelle Genomik (Prof. Oefner)
Identification Number:
ValueType
25465566PubMed ID
10.3168/jds.2014-8602DOI
Keywords:artificial neural network; prediction; ketosis; milk metabolite
Dewey Decimal Classification:500 Science > 570 Life sciences
600 Technology > 610 Medical sciences Medicine
Status:Published
Refereed:Yes, this version has been refereed
Created at the University of Regensburg:Partially
Item ID:31294
Owner only: item control page
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