Sentence examples for microbial association networks from inspiring English sources

The results showed that the four seasonal marine microbial association networks had characters of complex networks.

Here, we used MI [ 11] to compute the association relationship between variables and construct the seasonal marine microbial association networks.

In the end, we can construct the four marine microbial association networks of spring, summer, fall, and winter seasons.

In order to analyze the microbial diversity and the relationship among OTUs and environmental factors in spring, summer, fall, and winter seasons, we should construct the four seasonal marine microbial association networks.

The results show that the four seasonal marine microbial association networks have characters of complex networks, the same environmental factor influences different species in the four seasons, and the correlative relationships are stronger between OTUs (taxa) than with environmental factors in the four seasons detecting community.

The four seasonal marine microbial association networks with MI algorithm are shown in Figure 4. We also computed their topological parameters including the average degree, average clustering coefficient, average power law degree, and modularity and compared them with their corresponding random networks.

However, a six-year time-series study in which microbial association network analysis was carried out (Gilbert et al. 2012) showed that correlation in abundances was stronger within bacterial taxa than between bacteria and eukaryotes, or between bacteria and environmental factors.

Compared with random networks, four seasonal microbial correlation networks have bigger average clustering coefficient, average power law degree, and modularity, which indicate that the four seasonal microbial associate networks have some characters of complex network.

The increasing availability of time series microbial community data from metagenomics and other molecular biological studies has enabled the analysis of large-scale microbial co-occurrence and association networks.

The community structural analysis in four seasonal microbial networks shows that a large fraction microbial association in class level occurs among Alphaproteobacteria and Gammaproteobacteria; the community dense of summer, fall, and spring is bigger than that of winter; the correlative relationships are stronger between OTUs (taxa) than with environmental factors.

In this article we highlight the importance of a spatiotemporal sampling design, together with a multilevel 'omic approach and a community analysis strategy (association networks and modeling) to examine and predict interacting microbial communities and their impact on the environment.