ArticleGenetics, selection, evolution : GSE2023
Integrating on-farm and genomic information improves the predictive ability of milk infrared prediction of blood indicators of metabolic disorders in dairy cows.
Article in Genetics, selection, evolution : GSE, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Immunometabolic-uterine-ovarian interactions and flushing therapy in dairy cows: a narrative review.Veterinary world · 2026Review
- Weight Gain and Tenderness in Nelore Cattle: Genetic Association and a Potential Pleiotropic Role of Transcription Factors and Genes.Animals : an open access journal from MDPI · 2025Article
- The Use of Selected Machine Learning Methods in Dairy Cattle Farming: A Review.Animals : an open access journal from MDPI · 2025Review
- Variable selection strategies for genomic prediction of growth and carcass related traits in experimental Nellore cattle herds under different selection criteria.Scientific reports · 2025Article
- Combining genetic markers, on-farm information and infrared data for the in-line prediction of blood biomarkers of metabolic disorders in Holstein cattle.Journal of animal science and biotechnology · 2024Article
- Genomic prediction of blood biomarkers of metabolic disorders in Holstein cattle using parametric and nonparametric models.Genetics, selection, evolution : GSE · 2024Article
- Benchmarking machine learning and parametric methods for genomic prediction of feed efficiency-related traits in Nellore cattle.Scientific reports · 2024Article
- Milk Components and Fatty Acid Composition as Predictors of Days Open in Early Lactation Holstein Cows.Animal science journal = Nihon chikusan GakkaihoArticle
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundBlood metabolic profiles can be used to assess metabolic disorders and to evaluate the health status of dairy cows. Given that these analyses are time-consuming, expensive, and stressful for the cows, there has been increased interest in Fourier transform infrared (FTIR) spectroscopy of milk samples as a rapid, cost-effective alternative for predicting metabolic disturbances. The integration of FTIR data with other layers of information such as genomic and on-farm data (days in milk (DIM) and parity) has been proposed to further enhance the predictive ability of statistical methods. Here, we developed a phenotype prediction approach for a panel of blood metabolites based on a combination of milk FTIR data, on-farm data, and genomic information recorded on 1150 Holstein cows, using BayesB and gradient boosting machine (GBM) models, with tenfold, batch-out and herd-out cross-validation (CV) scenarios.
resultsThe predictive ability of these approaches was measured by the coefficient of determination (R
conclusionsOur results show that, compared to using only milk FTIR data, a model integrating milk FTIR spectra with on-farm and genomic information improves the prediction of blood metabolic traits in Holstein cattle and that GBM is more accurate in predicting blood metabolites than BayesB, especially for the batch-out CV and herd-out CV scenarios.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.