ArticleBMC genomics2019
Investigation of muscle transcriptomes using gradient boosting machine learning identifies molecular predictors of feed efficiency in growing pigs.
Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Machine Learning-Based Analysis of Differentially Expressed Genes in the Muscle Transcriptome Between Beef Cattle and Dairy Cattle.International journal of molecular sciences · 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
- Artificial intelligence: the human response to approach the complexity of big data in biology.GigaScience · 2025Review
- Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning.BioData mining · 2024Article
- African Local Pig Genetic Resources in the Context of Climate Change Adaptation.Animals : an open access journal from MDPI · 2024Review
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- Bovine FRAS1: mRNA Expression Profile, Genetic Variations, and Significant Correlations with Ovarian Morphological Traits, Mature Follicle, and Corpus Luteum.Animals : an open access journal from MDPI · 2024Article
- A genome-wide association study for loin depth and muscle pH in pigs from intensely selected purebred lines.Genetics, selection, evolution : GSE · 2023Article
- Rumen Microbiota Predicts Feed Efficiency of Primiparous Nordic Red Dairy Cows.Microorganisms · 2023Article
- Analysis of liver miRNA in Hu sheep with different residual feed intake.Frontiers in genetics · 2023Article
- Comparative Transcriptomic Analysis of mRNAs, miRNAs and lncRNAs in theBiomolecules · 2022Article
- Adaptation of Livestock to New Diets Using Feed Components without Competition with Human Edible Protein Sources-A Review of the Possibilities and Recommendations.Animals : an open access journal from MDPI · 2021Review
- Exploration of Genetic Variants within the Goat A-Kinase Anchoring Protein 12 (Animals : an open access journal from MDPI · 2021Article
- Analysis of merged whole blood transcriptomic datasets to identify circulating molecular biomarkers of feed efficiency in growing pigs.BMC genomics · 2021Article
- Identification of genomic regions affecting production traits in pigs divergently selected for feed efficiency.Genetics, selection, evolution : GSE · 2021Article
- Genome-wide association and transcriptome studies identify candidate genes and pathways for feed conversion ratio in pigs.BMC genomics · 2021Article
- Review
- Whole-genome SNP markers reveal conservation status, signatures of selection, and introgression in Chinese Laiwu pigs.Evolutionary applications · 2021Article
- Genomic Analysis Reveals Human-Mediated Introgression From European Commercial Pigs to Henan Indigenous Pigs.Frontiers in genetics · 2021Article
- TheFrontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundImproving feed efficiency (FE) is a major challenge in pig production. This complex trait is characterized by a high variability. Therefore, the identification of predictors of FE may be a relevant strategy to reduce phenotyping efforts in breeding and selection programs. The aim of this study was to investigate the suitability of expressed muscle genes in prediction of FE traits in growing pigs. The approach considered different transcriptomics experiments to cover a large range of FE values and identify reliable predictors.
resultsMicroarrays data were obtained from longissimus muscles of two lines divergently selected for residual feed intake (RFI). Pigs (n = 71) from three experiments belonged to generations 6 to 8 of selection, were fed either a diet with a standard composition or a diet rich in fiber and lipids, received feed ad libitum or at restricted level, and weighed between 80 and 115 kg at slaughter. For each pig, breeding value for RFI was estimated (RFI-BV), and feed conversion ratio (FCR) and energy-based feed conversion ratio (FCRe) were calculated during the test periods. Gradient boosting algorithms were used on the merged muscle transcriptomes to identify very important predictors of FE traits. About 20,405 annotated molecular probes were commonly expressed in longissimus muscle across experiments. Six to 267 expressed muscle genes covering a variety of biological processes were found as important predictors for RFI-BV (R
conclusionIt was possible to identify few genes expressed in muscle that might be reliable predictors of feed efficiency.
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