Suggesting valid pharmacogenes by mining linked data

International audienceA standard task in pharmacogenomics research is identifying genes that may be involved in drug response variability, i.e., pharmacogenes. Because genomic experiments tended to generate many false positives, computational approaches based on the use of background knowledge have been proposed. Until now, those have used only molecular networks or the biomedical literature. Here we propose a novel method that consumes an eclectic set of linked data sources to help validating uncertain drug–gene relationships. One of the advantages relies on that linked data are implemented in a standard framework that facilitates the joint use of various sources, making easy the consideration of features of various origins. Consequently, we propose an initial selection of linked data sources relevant to pharmacogenomics. We formatted these data to train a random forest algorithm , producing a model that enables classifying drug–gene pairs as related or not, thus confirming the validity of candidate pharmacogenes. Our model achieve the performance of F-measure=0.92, on a 100 folds cross-validation. A list of top candidates is provided and their obtention is discussed

Application of PCR-DGGE to the study of dynamics and biodiversity of microbial contaminants during the processing of Hibiscus sabdariffa drinks and concentrates

Introduction. Bissap (Hibiscus sabdariffa L.) is a common plant in the tropics. In Senegal, the calyces are used to make a popular juice. In the food industry, small and medium-sized enterprises (SMEs) are responsible for the transformation of bissap calyces into drinks, concentrates, jam, etc. In spite of the very low pH of the juice (pH < 3), problems of contamination and fermentation are often observed in the final products post-pasteurization. They are mainly due to Pseudomonas spp., E. coli, Klebsiella spp. and Pichia opuntiae. To solve this issue, monitoring of the microbial ecology was performed during the full process of bissap products. Methods and results. Fresh calyces and dried mixed calyces of the two varieties of Hibiscus sabdariffa ('Koor' and 'Vimto'), as well as juice samples, were collected at every stage of the processing of a bissap drink and syrup in a Senegalese SME. The monitoring of microbial flora was performed by using molecular fingerprinting. The molecular technique PCR-DGGE was employed to evaluate the microbial dynamics using bacterial 16S rDNA, yeast 26S rDNA and 28S rDNA mold profiles at each critical stage of the process. Results and discussion. The genetic profiles generated contributed to identifying the critical points in the manufacturing processes. A multivariate analysis based on the presence or absence of spots in the denaturing gradient electrophoresis gels (DGGE) showed that the microbial flora (bacteria, yeasts, molds) of bissap evolved during the following phases: harvest (fresh flower), drying (dried calyces) and processing (before filtration, after pasteurization and before packaging). Conclusion. Our work contributed to determining the microorganisms responsible for the microbial contamination of the final products, and highlighted the origin of these contaminants. The most important critical point was identified as the pasteurization step. (Résumé d'auteur

Genetic determinants of circulating haptoglobin concentration

Haptoglobin (Hp) is a major plasma acute-phase glycoprotein, which binds free haemoglobin to neutralize its toxicity. The HP gene exists as two copy number variants (CNV), HP1 and HP2, which differ in two ways: serum Hp level and functional differences in Hp protein products. Both mechanisms may underlie the HP CNV’s influence on susceptibility and/or outcome in several diseases. A single nucleotide polymorphism rs2000999 has also been associated with serum Hp level. In a meta-analysis of three studies from England, France and Japan, we show that rs2000999’s effect on circulating Hp level is independent from that of the HP CNV. The combined use of rs2000999 and the HP CNV can be an important genetic epidemiological tool to discriminate between the two potential mechanisms underlying differences between HP1 and HP2 alleles

Investigating ADR mechanisms with Explainable AI: a feasibility study with knowledge graph mining

National audienceAdverse drug reactions (ADRs) are statistically characterized within randomized clinical trials or by postmarketing pharmacovigilance. However, the molecular mechanisms causing ADRs remain unknown in most cases. This is true even for common toxicities that are classically monitored during trials such as hepatic or skin toxicities. Interestingly, many elements of knowledge about drugs and drug ingredients are available beside clinical trials. In particular, open-access knowledge graphs describe their properties, interactions, and involvements in pathways. Expert classifications have also been manually established by experts and label drugs either as causative or not for several types of ADRs. In our paper, we propose to mine biomedical knowledge graphs to identify biomolecular features that enable to automatically reproduce such expert classifications, distinguishing drugs causative or not for a given type of ADR. In an Explainable AI perspective, we explore simple classification techniques such as Decision Trees and Classification Rules because they provide human-readable models which explain the classification itself. We also evaluate the assumption that biomolecular features mined from knowledge graphs might provide elements of explanation for the molecular mechanisms behind ADRs

Кинотеатр с кафе на 100 мест в п. Шира РХ

SPARQL query example 2. This text file contains an example of SPARQL query that enable to explore the vicinity of an entity. This particular query returns the RDF graph surrounding, within a lenght of 4, the node pharmgkb:PA451906 that represents the warfarin, an anticoagulant drug. (TXT 392 bytes