Epidemiology of non-polio enterovirus infection in Ural federal district and West Siberia, 2021

The aim of the study – to describe epidemiology, and drivers of seasonality of enteroviruses causing infections in the Ural Federal District and Western SiberiaЦель исследования – охарактеризовать эпидемиологическую ситуацию с ЭВИ в Уральском федеральном округе и Западной Сибири на современном этап

Prevalence of non-polio enteroviruses among three to six years old healthy children from Yekaterinburg in 2021

The aim of the study – to estimate the prevalence of asymptomatic carriage of non-polio enteroviruses (NPEV) among healthy children in Yekaterinburg during the epidemic season of 2021.Цель исследования – оценить распространенность бессимптомного носительства энтеровирусов среди детей 3-6 лет в г. Екатеринбурге в эпидемический сезон 2021 г

Personalized microbial network inference via co-regularized spectral clustering

We use Human Microbiome Project (HMP) cohort [1] to infer personalized oral microbial networks of healthy individuals. To determine clustering of individuals with similar microbial profiles, co-regularized spectral clustering algorithm is applied to the dataset. For each cluster we discovered, we compute co-occurrence relationships among the microbial species that determine microbial network per cluster of individuals. The results of our study suggest that there are several differences in microbial interactions on personalized network level in healthy oral samples acquired from various niches. Based on the results of co-regularized spectral clustering we discover two groups of individuals with different topology of their microbial interaction network. The results of microbial network inference suggest that niche-wise interactions are different in these two groups. Our study shows that healthy individuals have different microbial clusters according to their oral microbiota. Such personalized microbial networks open a better understanding of the microbial ecology of healthy oral cavities and new possibilities for future targeted medication

Personalized microbial network inference via co-regularized spectral clustering

We use Human Microbiome Project (HMP) cohort (Peterson et al., 2009) to infer personalized oral microbial networks of healthy individuals. To determine clustering of individuals with similar microbial profiles, co-regularized spectral clustering algorithm is applied to the dataset. For each cluster we discovered, we compute co-occurrence relationships among the microbial species that determine microbial network per cluster of individuals. The results of our study suggest that there are several differences in microbial interactions on personalized network level in healthy oral samples acquired from various niches. Based on the results of co-regularized spectral clustering we discover two groups of individuals with different topology of their microbial interaction network. The results of microbial network inference suggest that niche-wise interactions are different in these two groups. Our study shows that healthy individuals have different microbial clusters according to their oral microbiota. Such personalized microbial networks open a better understanding of the microbial ecology of healthy oral cavities and new possibilities for future targeted medication. The scripts written in scientific Python and in Matlab, which were used for network visualization, are provided for download on the website http://learning-machines.com/

Online Semi-Supervised Learning: Algorithm and Application in Metagenomics

As the amount of metagenomic data grows rapidly, online statistical learning algorithms are poised to play key rolein metagenome analysis tasks. Frequently, data are only partially labeled, namely dataset contains partial information about the problem of interest. This work presents an algorithm and a learning framework that is naturally suitable for the analysis of large scale, partially labeled metagenome datasets. We propose an online multi-output algorithm that learns by sequentially co-regularizing prediction functions on unlabeled data points and provides improved performance in comparison to several supervised methods. We evaluate predictive performance of the proposed methods on NIH Human Microbiome Project dataset. In particular we address the task of predicting relative abundance of Porphyromonas species in the oral cavity. In our empirical evaluation the proposed method outperforms several supervised regression techniques as well as leads to notable computational benefits when training the predictive model