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Deciphering the Gene Expression and Alternative Splicing Basis of Muscle Development Through Interpretable Machine Learning Models

文献类型： 外文期刊

作者： Tan, Xiaodong 1 ; Huang, Minjie 1 ; Jin, Yuting 1 ; Li, Jiahua 1 ; Dong, Jie 1 ; Wang, Deqian 1 ;

作者机构： 1.Zhejiang Acad Agr Sci, Inst Anim Husb & Vet Sci, Hangzhou 310021, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Livestock & Poultry Resources Poultry Eval, Hangzhou 310021, Peoples R China

关键词： alternative splicing; breast muscle; chicken; machine learning; shapley additive exPlanations

期刊名称：BIOLOGY-BASEL （ 影响因子：3.5； 五年影响因子：4.0 ）

ISSN：

年卷期： 2025 年 14 卷 8 期

页码：

收录情况： SCI

摘要： In chickens, meat yield is a crucial trait in breeding programs. Identifying key molecular markers associated with increased muscle yield is essential for breeding strategies. This study applied transcriptome sequencing and machine learning methods to examine gene expression and alternative splicing (AS) events in muscle tissues of commercial broilers and local chickens. On the basis of differentially expressed genes (DEGs) and differentially spliced transcripts (DSTs) significantly related to breast muscle weight percentage (BrP), high-accuracy prediction models were developed by evaluating 10 machine learning models (e.g., eXtreme Gradient Boosting (XGBoost), Generalized Linear Model Network (Glmnet)). Feature importance was assessed using the Shapley Additive exPlanations (SHAP) method. The results revealed that 50 DEGs and 95 DSTs contributed significantly to BrP prediction. The XGBoost model achieved over 90% accuracy when using DEGs, and the Glmnet model reached 95% accuracy when using DSTs. Through Shapley evaluation, genes and AS events (e.g., ENSGALG00010012060, HINTW, and VIPR2-201) were identified as having the highest contributions to BrP prediction. Additionally, the breed effect was effectively mitigated. This study introduces new candidate genes and AS targets for the molecular breeding of poultry breast muscle traits, offering a paradigm shift from traditional gene mining approaches to artificial intelligence-driven predictive methods.

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