用于判别通路识别的机器学习流水线
A machine learning pipeline for discriminant pathways identification
摘要：
动机：确定在病理过程中更容易被破坏的分子通路是网络医学的一项关键任务，更一般地说，是系统生物学的一项关键任务。结果：在这项工作中，我们提出了一个管道，耦合机器学习解决方案的分子分析与最近的网络比较方法。该管道可以识别在病例对照生物标志物研究中构建的网络的特定子模块之间发生的变化，区分其相互作用被潜在条件改变的关键基因组。该建议是独立于所使用的分类算法。三个应用程序的全基因组数据提出了关于儿童对空气污染的易感性和两种神经退行性疾病：帕金森氏症和阿尔茨海默氏症。可用性：附录中提供了本文讨论的实验所用软件的详细信息。
英文摘要：
Motivation: Identifying the molecular pathways more prone to disruption during a pathological process is a key task in network medicine and, more in general, in systems biology. Results: In this work we propose a pipeline that couples a machine learning solution for molecular profiling with a recent network comparison method. The pipeline can identify changes occurring between specific sub-modules of networks built in a case-control biomarker study, discriminating key groups of genes whose interactions are modified by an underlying condition. The proposal is independent from the classification algorithm used. Three applications on genomewide data are presented regarding children susceptibility to air pollution and two neurodegenerative diseases: Parkinson's and Alzheimer's. Availability: Details about the software used for the experiments discussed in this paper are provided in the Appendix.
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