ArticleAnimals : an open access journal from MDPI2024
Comparative Transcriptome Analysis of Bovine, Porcine, and Sheep Muscle Using Interpretable Machine Learning Models.
Article in Animals : an open access journal from MDPI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
7 citing papers in PubMed.
- Integrated Histological and Transcriptomic Analyses Reveal Distinct Structural and Molecular Features of the Calipash and Muscle in the Chinese Soft-Shelled Turtle (Animals : an open access journal from MDPI · 2026Article
- Gene Expression Differences in Muscle and Adipose Tissue Help Explain Variation in Meat Tenderness Across USDA Carcass Grades in Beef and Fat Classes in Sheep.Animals : an open access journal from MDPI · 2026Article
- Transcriptomics and metabolomics provide insights into meat flavor precursor differences between different duck lines.Poultry science · 2026Article
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- Machine Learning-Based Analysis of Differentially Expressed Genes in the Muscle Transcriptome Between Beef Cattle and Dairy Cattle.International journal of molecular sciences · 2025Article
- Machine learning driven multi-omics analysis of the genetic mechanisms behind the double-coat fleece formation in Hetian sheep.Frontiers in genetics · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The growth and development of muscle tissue play a pivotal role in the economic value and quality of meat in agricultural animals, garnering close attention from breeders and researchers. The quality and palatability of muscle tissue directly determine the market competitiveness of meat products and the satisfaction of consumers. Therefore, a profound understanding and management of muscle growth is essential for enhancing the overall economic efficiency and product quality of the meat industry. Despite this, systematic research on muscle development-related genes across different species still needs to be improved. This study addresses this gap through extensive cross-species muscle transcriptome analysis, combined with interpretable machine learning models. Utilizing a comprehensive dataset of 275 publicly available transcriptomes derived from porcine, bovine, and ovine muscle tissues, encompassing samples from ten distinct muscle types such as the semimembranosus and longissimus dorsi, this study analyzes 113 porcine (
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Registered trials
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