对单个药物的药物反应预测模型的性能评估
Aron Park1, Yeeun Lee2, Seungyoon Nam3,4
1Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon, 21999, Republic of Korea.
Scientific reports
|July 24, 2023
概括
机器学习和深度学习模型在预测癌症细胞系中的个体药物反应方面表现相似. 该研究强调了这些模型在个性化癌症治疗中的潜力,通过确定药物反应的关键基因组特征.
科学领域:
- 药物基因组学 药物基因组学
- 计算生物学 计算生物学
- 癌症治疗 癌症治疗
背景情况:
- 在癌症治疗中,个性化医疗依赖于预测个体药物反应.
- 使用药物基因组学数据预测药物反应,特别是半最大抑制度 (IC50),至关重要但具有挑战性.
- 传统的机器学习 (ML) 和深度学习 (DL) 方法都用于预测,但它们对个体药物反应预测的比较性能尚未确立.
研究的目的:
- 构建和比较ML和DL模型以预测24种单个药物的药物反应 (IC50) 使用癌症细胞系的药物遗传学数据.
- 为了确定DL或传统ML模型是否为预测细胞活力提供更好的性能,IC50s.
- 应用可解释的人工智能 (XAI) 来识别影响药物反应的关键基因组特征.
主要方法:
- 开发了ML和DL模型来预测24种单个药物的药物反应 (IC50).
- 利用癌症细胞系的基因表达和突变特征作为输入特征.
- 使用诸如根平均平方误差 (RMSE) 和R平方 (R2) 等指标进行模型性能比较.
- 将可解释的人工智能 (XAI) 应用于表现最佳的模型,以识别重要的基因组特征.
主要成果:
- 在24种药物中,DL和ML模型之间没有观察到药物反应预测性能的显著差异.
- 对于DL,RMSE值在0.284至3.563之间,对于ML则在0.274至2.697之间.
- 对于DL,R2值在 -7.405到0.331之间,对于ML,R2值在 -8.113到0.470之间.
- 脊ML模型显示了最好的表现 (R2 = 0.470,RMSE = 0.623).
- XAI确定了22个基因的基因组特征,这些特征对于预测泛基因体反应至关重要.
结论:
- ML和DL模型在预测对单个药物的反应方面表现出相似的有效性.
- 该研究验证了药物反应预测模型对特定药物的适用性.
- 识别的基因组特征可以提供对药物反应机制的见解,支持个性化癌症治疗策略.
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