ArticleJournal of agricultural and food chemistry2022
Using Machine Learning to Identify Biomarkers Affecting Fat Deposition in Pigs by Integrating Multisource Transcriptome Information.
Article in Journal of agricultural and food chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Cross-Study Meta-Analysis of Blood Transcriptomes in Type 2 Diabetes.International journal of molecular sciences · 2025Pooled it
- Biomarkers of Adipose Color: A Multi-Omics Analysis Unravels the Molecular Landscape of White and Yellow Fat in Kazakh Horse.Biology · 2026Article
- Integrative transcriptomics and machine learning reveal key regulatory genes for meat quality traits in pigs.BMC genomics · 2026Article
- Heterosis-Based Identification of Candidate Genes Associated with Lipid Metabolism and Meat Quality in Crossbred Pigs.Animals : an open access journal from MDPI · 2026Article
- Complementary structure of statistical significance and predictive relevance in explainable machine learning-based transcriptomic tissue classification of Hanwoo cattle.Frontiers in genetics · 2026Article
- Article
- A deep learning strategy for accurate identification of purebred and hybrid pigs across SNP chips.Journal of animal science and biotechnology · 2025Article
- Can oxidative potential be a plant risk indicator for heavy metals contaminated soil? Analysis of ryegrass (Eco-Environment & Health · 2025Article
- Article
- Genome-Wide Transcriptome Profiling Reveals the Mechanisms Underlying Hepatic Metabolism under Different Raising Systems in Yak.Animals : an open access journal from MDPI · 2024Article
- Integration of transcriptome and machine learning to identify the potential key genes and regulatory networks affecting drip loss in pork.Journal of animal science · 2024Article
- Identification of key genes affecting intramuscular fat deposition in pigs using machine learning models.Frontiers in genetics · 2024Article
- Assessing Population Structure and Signatures of Selection in Wanbei Pigs Using Whole Genome Resequencing Data.Animals : an open access journal from MDPI · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fat deposition in pigs is not only closely related to pig production efficiency and pork quality but also an ideal model for human obesity. Transcriptome sequencing is widely used to study fat deposition. However, due to small sample sizes, high false positive rates, and poor consistency of results from different studies, new strategies are urgently needed. Machine learning, a new analysis method, can effectively fit complex data and accurately identify samples and genes. In this study, 36 samples of adipose tissue, muscle tissue, and liver tissue were collected from Songliao black pigs and Landrace pigs, and the mRNA of all the samples was sequenced. In addition, we collected transcriptome data for 64 samples in the GEO database from four different sources. After standardization and imputation of missing values in the data set comprising 100 samples, traditional differential expression analysis was carried out, and different numbers of expressed genes were selected as features for the training model of eight machine learning methods. In the 1000 replications of fourfold cross validation with 100 samples, AdaBoost performed best, with an average prediction accuracy greater than 93% and the highest mean area under the curve in predicting the high- and low-fat content groups among the eight ML methods. According to their performance-based ranks inferred by AdaBoost, 12 genes related to fat deposition were identified; among them,
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.