Integration of epigenomic and genomic data to predict residual feed intake and the feed conversion ratio in dairy sheep via machine learning algorithms - Fonseca, Pablo Augusto de Souza; Suarez-Vega, Aroa; Esteban-Blanco, Cristina; Marina, Héctor; Pelayo, Rocío; Gutiérrez-Gil, Beatriz; Arranz, Juan-José (BMC genomics, 2025)
Keywords: Accuracy; Algorithms; Animal Feed; Animal Genetics and Genomics; Animals; Artificial intelligence; Artificial neural networks; Biomarkers; Biomedical and Life Sciences; Bisulfite; Conversion ratio; Dairying; DNA Methylation; Eating - genetics; Efficiency; Enzymes; Epigenetics; Epigenomics - methods; Experiments; Feed conversion; Feed efficiency; Feeds; Female; Food and nutrition; Gene sequencing; Genes; Genetic aspects; Genetic diversity; Genetic research; Genetic variance; Genomes; Genomics - methods; Learning algorithms; Life Sciences; Livestock; Livestock production; Machine Learning; Microarrays; Microbial Genetics and Genomics; Milk; Multilayers; Neural networks; Nutrition research; Omics; Physiological aspects; Plant Genetics and Genomics; Prediction; Predictions; Proteins; Proteomics; Root-mean-square errors; Sheep; Sheep - genetics; Somatic cells; Spain; Livestock; Feed efficiency; Artificial intelligence; Omics; Prediction