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https://www.selleckchem.com/ BACKGROUND In silico functional genomics have become a driving force in the way we interpret and use gene expression data, enabling researchers to understand which biological pathways are likely to be affected by the treatments or conditions being studied. There are many approaches to functional genomics, but a number of popular methods determine if a set of modified genes has a higher than expected overlap with genes known to function as part of a pathway (functional enrichment testing). Recently, researchers have started to apply such analyses in a new way to ask if the data they are collecting show similar disruptions to biological functions compared to reference data. Examples include studying whether similar pathways are perturbed in smokers vs. users of e-cigarettes, or whether a new mouse model of schizophrenia is justified, based on its similarity in cytokine expression to a previously published model. However, there is a dearth of robust statistical methods for testing hypotheses related to these queS ECEA provides a new way to perform gene enrichment analysis that allows researchers to compare their data to existing datasets and determine if a treatment will cause similar or opposing genomic perturbations.BACKGROUND Respiratory infections are a major threat for lung recipients. We aimed to compare with a monocentric study the impact of late viral and bacterial respiratory infections on the graft function. METHODS Patients, who survived 6 months or more following lung transplantation that took place between 2009 and 2014, were classified into three groups a viral infection group (VIG) (without any respiratory bacteria), a bacterial infection group (BIG) (with or without any respiratory viruses), and a control group (CG) (no documented infection). Chronic lung allograft dysfunction (CLAD) and acute rejection were analysed 6 months after the inclusion in the study. RESULTS Among 99 included lung recipients, 57 (58%) had at least o

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