High-throughput technologies have been used to generate a large amount of omics data. In the past, single-level analysis has been extensively conducted where the omics measurements at different levels, including mRNA, microRNA, CNV and DNA methylation, are analyzed separately. As the molecular complexity of disease etiology exists at all different levels, integrative analysis offers an effective way to borrow strength across multi-level omics data and can be more powerful than single level analysis. In this article, we focus on reviewing existing multi-omics integration studies by paying special attention to variable selection methods. We first summarize published reviews on integrating multi-level omics data. Next, after a brief overview on variable selection methods, we review existing supervised, semi-supervised and unsupervised integrative analyses within parallel and hierarchical integration studies, respectively. The strength and limitations of the methods are discussed in detail. No existing integration method can dominate the rest. The computation aspects are also investigated. The review concludes with possible limitations and future directions for multi-level omics data integration.

The acquisition of antibiotic resistance (AR) by foodborne pathogens, such as Salmonella enterica, has emerged as a serious public health concern. The relationship between the two key survival mechanisms (i.e., antibiotic resistance and virulence) of bacterial pathogens is complex. However, it is unclear if the presence of certain virulence determinants (i.e., virulence genes) and AR have any association in Salmonella. In this study, we report the prevalence of selected virulence genes and their association with AR in a set of phenotypically tested antibiotic-resistant (n = 117) and antibiotic-susceptible (n = 94) clinical isolates of Salmonella collected from Tennessee, USA. Profiling of virulence genes (i.e., virulotyping) in Salmonella isolates (n = 211) was conducted by targeting 13 known virulence genes and a gene for class 1 integron. The association of the presence/absence of virulence genes in an isolate with their AR phenotypes was determined by the machine learning algorithm Random Forest. The analysis revealed that Salmonella virulotypes with gene clusters consisting of avrA, gipA, sodC1, and sopE1 were strongly associated with any resistant phenotypes. To conclude, the results of this exploratory study shed light on the association of specific virulence genes with drug-resistant phenotypes of Salmonella. The presence of certain virulence genes clusters in resistant isolates may become useful for the risk assessment and management of salmonellosis caused by drug-resistant Salmonella in humans.

Elderly people had suffered a disproportionate burden of COVID-19. We hypothesized that males and females in different age groups might have different epidemic trajectories. Using publicly available data from South Korea, daily new COVID-19 cases were assessed using generalized additive models, assuming Poisson and negative binomial distributions. Epidemic dynamics by age and gender groups were explored using interactions between smoothed time terms and age and gender. A negative binomial distribution fitted the daily case counts best. The relationship between the dynamic patterns of daily new cases and age groups was statistically significant (p < 0.001), but this was not the case with gender groups. People aged 20–39 years led the epidemic processes in South Korean society with two peaks — one major peak around March 1 and a smaller peak around April 7, 2020. The epidemic process among people aged 60 or above trailed behind that of the younger age group, and with smaller magnitude. After March 15, there was a consistent decline of daily new cases among elderly people, despite large fluctuations in case counts among young adults. Although young people drove the COVID-19 epidemic throughout society, with multiple rebounds, elderly people could still be protected from infection after the peak of the epidemic.