BACKGROUND Microbes are greatly associated with human health and disease, especially in densely populated cities. It is essential to understand the microbial ecosystem in an urban environment for cities to monitor the transmission of infectious diseases and detect potentially urgent threats. To achieve this goal, the DNA sample collection and analysis have been conducted at subway stations in major cities. However, city-scale sampling with the fine-grained geo-spatial resolution is expensive and laborious. In this paper, we introduce MetaMLAnn, a neural network based approach to infer microbial communities at unsampled locations given information reflecting different factors, including subway line networks, sampling material types, and microbial composition patterns. RESULTS We evaluate the effectiveness of MetaMLAnn based on the public metagenomics dataset collected from multiple locations in the New York and Boston subway systems. The experimental results suggest that MetaMLAnn consistently performs better than other five conventional classifiers under different taxonomic ranks. At genus level, MetaMLAnn can achieve F1 scores of 0.63 and 0.72 on the New York and the Boston datasets, respectively. CONCLUSIONS By exploiting heterogeneous features, MetaMLAnn captures the hidden interactions between microbial compositions and the urban environment, which enables precise predictions of microbial communities at unmeasured locations.

中文翻译：

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使用神经网络方法预测城市宏基因组学的微生物群落。

背景技术微生物特别是在人口稠密的城市中与人类健康和疾病密切相关。必须了解城市环境中的微生物生态系统，以便城市监测传染病的传播并发现潜在的紧急威胁。为了实现这一目标，已经在主要城市的地铁站进行了DNA样本的收集和分析。但是，具有细粒度地理空间分辨率的城市规模采样既昂贵又费力。在本文中，我们介绍了MetaMLAnn，这是一种基于神经网络的方法，可在未反映采样点的情况下推断出微生物群落，并提供反映不同因素的信息，包括地铁线网络，采样材料类型和微生物组成模式。结果我们基于从纽约和波士顿地铁系统的多个位置收集的公共宏基因组学数据集评估MetaMLAnn的有效性。实验结果表明，在不同的生物分类等级下，MetaMLAnn的性能始终优于其他五个常规分类器。在属水平上，MetaMLAnn可以在纽约和波士顿数据集上分别获得0.63和0.72的F1分数。结论通过利用异构特征，MetaMLAnn捕获了微生物成分与城市环境之间的隐藏相互作用，从而能够精确预测未测位置的微生物群落。实验结果表明，在不同的生物分类等级下，MetaMLAnn的性能始终优于其他五个常规分类器。在属水平上，MetaMLAnn可以在纽约和波士顿数据集上分别获得0.63和0.72的F1分数。结论通过利用异构特征，MetaMLAnn捕获了微生物成分与城市环境之间的隐藏相互作用，从而能够精确预测未测位置的微生物群落。实验结果表明，在不同的生物分类等级下，MetaMLAnn的性能始终优于其他五个常规分类器。在属水平上，MetaMLAnn可以在纽约和波士顿数据集上分别获得0.63和0.72的F1分数。结论通过利用异构特征，MetaMLAnn捕获了微生物成分与城市环境之间的隐藏相互作用，从而能够精确预测未测位置的微生物群落。