According to the advances of high-throughput sequencing technology, massive microbiome data accumulated from environmental investigations to human studies. The microbiome-wide association studies are to study the relationship between the microbiome and human health or environment. Recently, Deep Neural Networks (DNNs) are encouraging due to their layer-wise learning ability for representation learning. However, DNNs are considered as black boxes and they require a large amount of training data which makes them impractical to conduct microbiome-wide association studies directly. Meanwhile, the microbiome data is high dimension with many features and noise. A single feature selection method for dealing with the kind of dataset is often unstable. In this work, we introduced a deep learning model named Deep Forest to conduct the microbiome-wide association studies and an ensemble feature selection method is proposed to guide microbial biomarkers' identification. The experiments showed that our ensemble feature method based on Deep Forest had good stability and robustness. The results of feature selection could guide the discovery of microbial biomarkers and help to diagnose microbial-related diseases. The code is available at https://github.com/MicroAVA/MWAS-Biomarkers.git.

中文翻译：

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用于微生物组关联研究的强大生物标志物发现

随着高通量测序技术的进步，从环境调查到人体研究积累了海量微生物组数据。全微生物组关联研究是研究微生物组与人类健康或环境之间的关系。最近，深度神经网络 (DNN) 因其对表征学习的分层学习能力而令人鼓舞。然而，DNN 被认为是黑匣子，它们需要大量的训练数据，这使得它们无法直接进行微生物组范围的关联研究。同时，微生物组数据是高维的，具有许多特征和噪声。处理此类数据集的单一特征选择方法通常是不稳定的。在这项工作中，我们引入了一种名为 Deep Forest 的深度学习模型来进行微生物组范围的关联研究，并提出了一种集成特征选择方法来指导微生物生物标志物的识别。实验表明，我们基于 Deep Forest 的集成特征方法具有良好的稳定性和鲁棒性。特征选择的结果可以指导微生物生物标志物的发现，有助于诊断微生物相关疾病。代码可在 https://github.com/MicroAVA/MWAS-Biomarkers.git 获得。特征选择的结果可以指导微生物生物标志物的发现，有助于诊断微生物相关疾病。代码可在 https://github.com/MicroAVA/MWAS-Biomarkers.git 获得。特征选择的结果可以指导微生物生物标志物的发现，有助于诊断微生物相关疾病。代码可在 https://github.com/MicroAVA/MWAS-Biomarkers.git 获得。