ArticleScientific reports2021
Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Early prediction of final weight in nomadic sheep flocks using machine learning algorithms.Tropical animal health and production · 2025Article
- Machine learning-based early prediction of growth and morphological traits at yearling age in pure and hybrid goat offspring.Tropical animal health and production · 2024Article
- The Use of Machine Learning to Predict Prevalence of Subclinical Mastitis in Dairy Sheep Farms.Animals : an open access journal from MDPI · 2024Article
- Matrix-assisted laser desorption ionization-time-of-flight mass spectrometry in veterinary medicine: Recent advances (2019-present).Veterinary world · 2022Review
- Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study.Sensors (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Streptococcus uberis is one of the leading pathogens causing mastitis worldwide. Identification of S. uberis strains that fail to respond to treatment with antibiotics is essential for better decision making and treatment selection. We demonstrate that the combination of supervised machine learning and matrix-assisted laser desorption ionization/time of flight (MALDI-TOF) mass spectrometry can discriminate strains of S. uberis causing clinical mastitis that are likely to be responsive or unresponsive to treatment. Diagnostics prediction systems trained on 90 individuals from 26 different farms achieved up to 86.2% and 71.5% in terms of accuracy and Cohen's kappa. The performance was further increased by adding metadata (parity, somatic cell count of previous lactation and count of positive mastitis cases) to encoded MALDI-TOF spectra, which increased accuracy and Cohen's kappa to 92.2% and 84.1% respectively. A computational framework integrating protein-protein networks and structural protein information to the machine learning results unveiled the molecular determinants underlying the responsive and unresponsive phenotypes.
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Registered trials
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