通过可解释的机器学习模型解读肌肉发育的基因表达和替代拼接基础
Xiaodong Tan1,2, Minjie Huang1,2, Yuting Jin1,2
1Institute of Animal Husbandry and Veterinary Science, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China.
Biology
|September 4, 2025
概括
这项研究使用机器学习来识别与胸肌肉产量相关的关键基因和拼接事件. 人工智能模型可以准确预测肌肉的百分比,
科学领域:
- 基因组学
- 生物信息学
- 动物养殖
背景情况:
- 肉类产量对于家禽养殖计划至关重要.
- 识别肌肉产量的分子标记对于遗传改进至关重要.
研究的目的:
- 应用转录组测序和机器学习来识别胸肌肉重量百分比 (BrP) 的分子标记.
- 使用基因表达和替代拼接数据,开发准确的BrP预测模型.
主要方法:
- 肉和当地肌肉组织的转录组测序.
- 不同基因表达 (DEG) 和替代拼接 (AS) 分析.
- 机器学习模型开发 (例如XGBoost,Glmnet) 和特征重要性评估 (SHAP).
主要成果:
- 确定了50个与BrP显著相关的DEG和95个差异拼接转录 (DST).
- 使用DEG (XGBoost) 和使用DST (Glmnet) 实现了90%以上的BrP预测准确度.
- 突出的关键基因 (例如,ENSGALG00012060,HINTW,VIPR2-201) 和导致BrP的AS事件
结论:
- 开发了高精度的人工智能驱动的预测模型,用于家禽的乳腺肌肉特征.
- 确定了新型候选基因和AS分子繁殖的目标.
- 在识别经济重要特征的遗传标记方面向人工智能转变.
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