使用人工智能 (AI) 进行药物遗传学分析,以识别与低最佳卵巢反应和高反应相关的多态性
José A Ortiz1, B Lledó2, L Luque3
1Molecular Biology, Instituto Bernabeu, Avda. Albufereta 31, 03016, Alicante, Spain. jaortiz@institutobernabeu.com.
Journal of assisted reproduction and genetics
|April 2, 2025
概括
机器学习确定了六种影响卵巢反应的遗传变异在卵子捐赠者. 这些遗传标记可以帮助个性化生育治疗,以防止低于最佳或高反应.
科学领域:
- 生殖生物学 生殖生物学
- 遗传学 是一个遗传学.
- 机器学习在医学中的应用
背景情况:
- 卵巢反应的变化影响生育治疗.
- 识别遗传预测因素可以优化卵巢刺激.
研究的目的:
- 使用机器学习识别与低于最佳或过度卵巢反应相关的遗传变异.
- 开发一个预测模型,用于卵巢对卵子细胞捐赠者的反应.
主要方法:
- 对495个卵细胞捐赠的回顾性分析 (2018年3月至2022年4月).
- 已知调节卵巢反应的31种多态的基因定型.
- 应用5个机器学习算法来预测响应,其中SHAP值具有变量重要性.
主要成果:
- 15.15%的患者经历了低于最佳的反应,而27.27%的患者在类似的条件下经历了超级反应.
- 随机森林模型实现了AUC的0.822.2.
- 发现了六种遗传多态:ESR2,FSHR与低于最佳反应相关;AMHR2,GDF9与超级反应相关.
结论:
- 六种遗传多态性与卵巢反应有显著的关联.
- 这些发现支持个性化卵巢刺激协议.
- 基因查可以帮助预防不充分或过度的卵巢反应.
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