Mining biological information from rich "-omics" datasets is facilitated by organizing features into groups that are related to a biological phenomenon or clinical outcome. For example, microorganisms can be grouped based on a phylogenetic tree that depicts their similarities regarding genetic or physical characteristics. Here, we describe algorithms that incorporate auxiliary information in terms of groups of predictors and the relationships between them into the metagenome learning task to build intelligible models. In particular, our cost function guides the feature selection process using auxiliary information by requiring related groups of predictors to provide similar contributions to the final response. We apply the developed algorithms to a recently published dataset analyzing the effects of fecal microbiota transplantation (FMT) in order to identify factors that are associated with improved peripheral insulin sensitivity, leading to accurate predictions of the response to the FMT.

中文翻译：

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领域可理解模型

通过将特征组织成与生物现象或临床结果相关的组，有助于从丰富的“组学”数据集中挖掘生物信息。例如，可以根据描述微生物在遗传或物理特征方面的相似性的系统发育树对微生物进行分组。在这里，我们描述了将预测变量组和它们之间的关系方面的辅助信息合并到宏基因组学习任务中以构建可理解模型的算法。特别是，我们的成本函数通过要求相关的预测变量组对最终响应提供类似的贡献来使用辅助信息指导特征选择过程。