Medical genetics

We propose a statistical explorative method for data integration. It is developed in the context of early drug development for which it enables the detection of chemical substructures and the identification of genes that mediate their association with the bioactivity (BA). The core of the method is a sparse singular value decomposition for the identification of the gene set and a permutation-based method for the control of the false discovery rate. The method is illustrated using a real dataset, and its properties are empirically evaluated by means of a simulation study.

Background: Asthma is classified according to severity and inflammatory phenotype and is likely to be distinguished by specific microRNA (miRNA) expression profiles.

Objective: We sought to associate miRNA expression in sputum supernatants with the inflammatory cell profile and disease severity in asthmatic patients.

Background: Several microbial studies reported gut microbiota dysbiosis in patients with cystic fibrosis (CF). The functional consequences of this phenomenon are poorly understood. Faecal metaproteomics allows the quantitative analysis of host and microbial proteins to address functional changes resulting from this dysbiosis.