Abstract
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 , 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 based on the public metagenomics dataset collected from multiple locations in the New York and Boston subway systems. The experimental results suggest that consistently performs better than other five conventional classifiers under different taxonomic ranks. At genus level, can achieve F1 scores of 0.63 and 0.72 on the New York and the Boston datasets, respectively.
Conclusions
By exploiting heterogeneous features, captures the hidden interactions between microbial compositions and the urban environment, which enables precise predictions of microbial communities at unmeasured locations.
Black Carbon and Particulate Matter (PM2.5) Concentrations in New York City’s Subway Stations