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基于转录学学的可解释机器学习管道预测狼患者的表型

Emily L Leventhal¹, Andrea R Daamen¹, Amrie C Grammer¹

¹AMPEL BioSolutions LLC, and the RILITE Research Institute, Charlottesville, VA 22902, USA.

|October 20, 2023

概括

机器学习模型可以使用血液基因表达来预测系统性红斑狼 (SLE) 现型. 关键的免疫和代谢途径准确地确定了疾病活动和器官参与.

科学领域:

计算生物学是一种计算生物学.
免疫学免疫学免疫学
基因组学就是基因组学.

背景情况:

机器学习 (ML) 可以从基因表达数据中识别患者子组.
目前的ML方法往往缺乏用于表型预测的系统生物学背景.

研究的目的:

开发一种可解释的ML方法,使用血液转录学来预测系统性红斑狼 (SLE) 的表型.
通过评估预定义的基因组以获得预测能力,整合系统生物学背景.

主要方法:

使用了一种顺序分组的特征重要性算法.
评估基因组 (免疫,代谢途径,细胞类型) 在预测SLE疾病活性和器官参与方面的性能.
专注于血液转录组学,用于表型预测.

主要成果:

鉴定了与干扰素,瘤亡因子,线粒体和T细胞激活相关的基因组,作为SLE表型的顶级预测因素.
在预测疾病活动和器官参与方面取得了出色的表现.
证明了可解释的ML在理解疾病异质性的有用性.

结论:

开发的ML方法为与SLE表现相关的分子途径提供了洞察力.
这种可解释的ML策略可以应用于其他复杂疾病和组织的表型预测.
强调系统生物学背景在基于机器学习的生物医学研究中的重要性.

关键词:

临床遗传学临床遗传学计算生物信息学是指计算机生物信息学.免疫系统疾病免疫系统疾病免疫学免疫学免疫学

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