ArticleHeliyon2024
Identification of potential tissue-specific biomarkers involved in pig fat deposition through integrated bioinformatics analysis and machine learning.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
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Who cites it
3 citing papers in PubMed.
- Runs of homozygosity reveal candidate genes for economic traits in Danish Large White pigs.Archives animal breeding · 2026Article
- Transcriptomic analysis of muscle and adipose tissues identifies myokines and adipokines contributing to lipid deposition in Taoyuan Black pigs.Animal nutrition (Zhongguo xu mu shou yi xue hui) · 2025Article
- Genetic biomarker prediction based on gender disparity in asthma throughout machine learning.Frontiers in medicine · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Backfat thickness (BT) and intramuscular fat (IMF) content are closely appertained to meat production and quality in pig production. Deposition in subcutaneous adipose (SA) and IMF concerns different genes and regulatory mechanisms. And larger studies with rigorous design should be carried to explore the molecular regulation of fat deposition in different tissues. The purpose of this study is to gain a better understanding of the molecular mechanisms underlying differences in fat deposition among different tissues and identify tissue-specific genes involved in regulating fat deposition. The SA-associated datasets (GSE122349 and GSE145956) and IMF-associated datasets (GSE165613 and GSE207279) were downloaded from the Gene Expression Omnibus (GEO) as the BT and IMF group, respectively. Subsequently, the Robust Rank Aggregation (RRA) algorithm identified 27 down- and 29 up-regulated differentially expressed genes (DEGs) in the BT group. Based on bioinformatics and three machine learning algorithms, four SA deposition-related potential biomarkers, namely
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