Biological data integration using Semantic Web technologies

International audienceCurrent research in biology heavily depends on the availability and efficient use of information. In order to build new knowledge, various sources of biological data must often be combined. Semantic Web technologies, which provide a common framework allowing data to be shared and reused between applications, can be applied to the management of disseminated biological data. However, due to some specificities of biological data, the application of these technologies to life science constitutes a real challenge. Through a use case of biological data integration, we show in this paper that current Semantic Web technologies start to become mature and can be applied for the development of large applications. However, in order to get the best from these technologies, improvements are needed both at the level of tool performance and knowledge modeling

Prediction of miRNA-disease associations with a vector space model

MicroRNAs play critical roles in many physiological processes. Their dysregulations are also closely related to the development and progression of various human diseases, including cancer. Therefore, identifying new microRNAs that are associated with diseases contributes to a better understanding of pathogenicity mechanisms. MicroRNAs also represent a tremendous opportunity in biotechnology for early diagnosis. To date, several in silico methods have been developed to address the issue of microRNA-disease association prediction. However, these methods have various limitations. In this study, we investigate the hypothesis that information attached to miRNAs and diseases can be revealed by distributional semantics. Our basic approach is to represent distributional information on miRNAs and diseases in a high-dimensional vector space and to define associations between miRNAs and diseases in terms of their vector similarity. Cross validations performed on a dataset of known miRNA-disease associations demonstrate the excellent performance of our method. Moreover, the case study focused on breast cancer confirms the ability of our method to discover new disease-miRNA associations and to identify putative false associations reported in databases

Analyse des groupes de gènes co-exprimés : un outil automatique pour l'interprétation des expériences de biopuces (version étendue)

National audienceLa technologie des biopuces permet de mesurer les niveaux d'expression de milliers de gènes dans différentes conditions biologiques générant ainsi des masses de données à analyser. De nos jours, l'interprétation de ces volumineux jeux de donnés à la lumière des différentes sources d'informations est l'un des principaux défis dans la bio-informatique. Nous avons développé une nouvelle méthode appelée AGGC (Analyse des Groupes de Gènes Co-exprimés) qui permet de constituer de manière automatique des groupes de gènes à la fois fonctionnellement riches, i.e. qui partagent les mêmes annotations fonctionnelles, et co-exprimés. AGGC intègre l'information issue des biopuces, i.e. les profils d'expression des gènes, avec les annotations fonctionnelles des gènes obtenues à partir des sources d'informations génomiques comme Gene Ontology. Les expérimentations menées avec cette méthode ont permis de mettre en évidence les principaux groupes de gènes fonctionnellement riches et co-exprimés dans des expériences de biopuces. Programme et informations annexes : http://keia.i3s.unice.fr/?Implementations:CGGA

Suppression of superconductivity by non-magnetic disorder in the organic superconductor (TMTSF)2(ClO4)(1-x)(ReO4)x

We present a study of the superconducting properties (Tc and Hc2) in the solid solution (TMTSF)2(ClO4)(1-x)(ReO4)x with a ReO-4 nominal concentration up to x = 6%. The dramatic suppression of Tc when the residual resistivity is increased upon alloying with no modification of the Fermi surface is the signature of non-conventional superconductivity . This behaviour strongly supports p or d wave pairing in quasi one dimensional organic superconductors. The determination of the electron lifetime in the normal state at low temperature confirms that a single particle Drude model is unable to explain the temperature dependence of the conductivity and that a very narrow zero frequency mode must be taken into account for the interpretation of the transport properties.Comment: Received 26 January 2004 / Received in final form 17 June 2004 / Published online 3 August 200

Co-expressed Gene Groups Analysis (CGGA): An Automatic Tool for the Interpretation of Microarray Experiments

International audienceMicroarray technology produces vast amounts of data by measuring simultaneously the expression levels of thousands of genes under hundreds of biological conditions. Nowadays, one of the principal challenges in bioinformatics is the interpretation of this large amount of data using different sources of information. We have developed a novel data analysis method named CGGA (Co-expressed Gene Groups Analysis) that automatically finds groups of genes that are functionally enriched, i.e. have the same functional annotations, and are co-expressed. CGGA automatically integrates the information of microarrays, i.e. gene expression profiles, with the functional annotations of the genes obtained by the genome-wide information sources such as Gene Ontology. By applying CGGA to well-known microarray experiments, we have identified the principal functionally enriched and co-expressed gene groups, and we have shown that this approach enhances and accelerates the interpretation of DNA microarray experiments. CGGA program is available at http://www.i3s.unice.fr/~rmartine/CGG

Approaching the limit of CuII/CuImixed valency in a CuIBr2–N-methylquinoxalinium hybrid compound

A novel 1D hybrid salt (MQ)[CuBr2]∞ (MQ = N-methylquinoxalinium) is reported. Structural, spectroscopic and magnetic investigations reveal a minimal CuII doping of less than 0.1%. However it is not possible to distinguish CuI and CuII. The unusually close packing of the organic moieties and the dark brown colour of the crystals suggest a defect electronic structure