MuTATE是一个R包,用于全面的多目标分子建模.
Sarah G Ayton1,2, Víctor Treviño1,3
1Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Mexico.
Bioinformatics (Oxford, England)
|September 9, 2023
概括
我们开发了用于自动分子建模的多目标自动树引擎 (MuTATE). 这种工具可以识别多个临床终点的疾病亚组和生物标志物,改善精准医学和生物标志物发现.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 机器学习是机器学习.
背景情况:
- 多omics研究推进了精准医学,但由于解释能力有限,挑战了当前的机器学习模型.
- 现有的工具难以同时识别疾病亚组,生物标志物,并解释多个临床终点.
- 目前的方法需要先进的计算技能和广泛的手动解释.
研究的目的:
- 开发用于分子建模的自动化,全面的机器学习方法.
- 为了能够同时识别疾病亚组及其定义分子生物标志物.
- 提供一个可解释的模型,解释精准医学的多种临床终点.
主要方法:
- 开发了多目标自动树引擎 (MuTATE),用于自动决策树的构建.
- 纳入多个目标和目标权重用于多目标建模.
- 启用了分子生物标志物和患者子组之间的关系的用户友好可视化.
主要成果:
- MuTATE 通过可解释的决策树来促进自动化和全面的分子建模.
- 该工具可视化了分子生物标志物和多个临床终点的患者子组之间的关系.
- 通过消除对多个不可解释模型的手动合成的需求,MuTATE提高了效率.
结论:
- MuTATE为复杂疾病建模提供了灵活和多功能解决方案,包括癌症.
- 该工具有可能将生物标志物发现和亚型识别转化为个性化治疗策略.
- MuTATE促进了对疾病机制的理解,并改善了精准医学中的治疗决策.
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