ArticleFrontiers in genetics2021
Identification of Predictor Genes for Feed Efficiency in Beef Cattle by Applying Machine Learning Methods to Multi-Tissue Transcriptome Data.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Integrating biomarkers and artificial intelligence for precision diagnostics in cattle health and herd management: a review.Veterinary research communications · 2026Review
- Complementary structure of statistical significance and predictive relevance in explainable machine learning-based transcriptomic tissue classification of Hanwoo cattle.Frontiers in genetics · 2026Article
- From machine learning to digital twin integration for livestock production and research.Frontiers in veterinary science · 2026Review
- Advancements in Animal Breeding: From Mendelian Genetics to Machine Learning.International journal of molecular sciences · 2025Review
- Maternal administration of octanoate, a medium-chain fatty acid, improves feed efficiency of Japanese black calves through influencing gut bacteriome structure.Scientific reports · 2025Article
- Single-Step Genome-Wide Association Study of Factors for Evaluated and Linearly Scored Traits in Swedish Warmblood Horses.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2025Article
- Integration of epigenomic and genomic data to predict residual feed intake and the feed conversion ratio in dairy sheep via machine learning algorithms.BMC genomics · 2025Article
- TransGeneSelector: using a transformer approach to mine key genes from small transcriptomic datasets in plant responses to various environments.BMC genomics · 2025Article
- Leveraging Transcriptional Signatures of Diverse Stressors for Bumble Bee Conservation.Molecular ecology · 2025Article
- Lambs Grazing With Adult Ewes Prefer Forbs With High-Nutrient Content in Native Grasslands Dominated byEcology and evolution · 2024Article
- Transcriptional response to an alternative diet on liver, muscle, and rumen of beef cattle.Scientific reports · 2024Article
- Rumen Microbiota Predicts Feed Efficiency of Primiparous Nordic Red Dairy Cows.Microorganisms · 2023Article
- Integration of the Microbiome, Metabolome and Transcriptome RevealsAnimals : an open access journal from MDPI · 2023Article
- Transcriptome Profiling of the Liver in Nellore Cattle Phenotypically Divergent for RFI in Two Genetic Groups.Animals : an open access journal from MDPI · 2023Article
- Predicting dry matter intake in beef cattle.Journal of animal science · 2023Article
- Feed efficiency in dairy sheep: An insight from the milk transcriptome.Frontiers in veterinary science · 2023Article
- A review of machine learning models applied to genomic prediction in animal breeding.Frontiers in genetics · 2023Review
- Article
- Elucidating genes and gene networks linked to individual susceptibility to milk fat depression in dairy goats.Frontiers in veterinary science · 2022Article
- Non-coding transcriptomic profiles in the sheep mammary gland during different lactation periods.Frontiers in veterinary science · 2022Article
Corrections and comments
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) methods have shown promising results in identifying genes when applied to large transcriptome datasets. However, no attempt has been made to compare the performance of combining different ML methods together in the prediction of high feed efficiency (HFE) and low feed efficiency (LFE) animals. In this study, using RNA sequencing data of five tissues (adrenal gland, hypothalamus, liver, skeletal muscle, and pituitary) from nine HFE and nine LFE Nellore bulls, we evaluated the prediction accuracies of five analytical methods in classifying FE animals. These included two conventional methods for differential gene expression (DGE) analysis (
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