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结合临床和分子数据,为急性髓性白血病提供个性化治疗:一种机器学习方法

Nestoras Karathanasis¹, Panayiota L Papasavva², Anastasis Oulas¹

¹Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371 Ayios Dometios, Nicosia, Cyprus.

Computer methods and programs in biomedicine

|September 24, 2024

概括

机器学习准确地预测了急性髓性白血病 (AML) 患者的药物敏感性. 这种方法通过基于分子和临床数据识别更有效的药物来个性化治疗,从而改善患者的治疗结果.

科学领域:

计算生物学是一种计算生物学.
基因组学就是基因组学.
药理学药理学是指药理学的学科.

背景情况:

几十年来,标准的急性髓性白血病 (AML) 治疗方法几乎没有变化.
复杂的突变和缺乏向疗法阻碍了个性化的AML治疗.
BeatAML数据集提供了广泛的分子,临床和药物敏感性数据.

研究的目的:

使用机器学习 (ML) 算法重新分析BeatAML数据集.
在AML患者中预测122种药物的活体药物敏感性.
为个性化治疗策略利用分子和临床数据.

主要方法:

使用ElasticNet进行完全可解释的ML模型.
使用了自动基因过的两步训练协议.
评估所有数据组合以优化每个药物的训练设置.

主要成果:

通过结合临床和RNA测序数据,实现了0.67的皮尔森相关性.
RNA测序数据显示,整个外基因组测序的预测能力是整个外基因组测序的三倍.
确定了88%的患者预测药物比给药药物更有效.

结论:

对BeatAML数据集的ML再分析显示了个性化AML治疗的潜力.
该方法显示了预测和实际药物反应之间的有希望的相关性.
这代表了改善AML患者治疗结果的重大进展.

关键词:

在急性髓性白血病中.药物反应的药物反应机器学习机器学习

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