BioQuali Cytoscape plugin: analysing the global consistency of regulatory networks

International audienceBackground: The method most commonly used to analyse regulatory networks is the in silico simulation of fluctuations in network components when a network is perturbed. Nevertheless, confronting experimental data with a regulatory network entails many difficulties, such as the incomplete state-of-art of regulatory knowledge, the large-scale of regulatory models, heterogeneity in the available data and the sometimes violated assumption that mRNA expression is correlated to protein activity. Results: We have developed a plugin for the Cytoscape environment, designed to facilitate automatic reasoning on regulatory networks. The BioQuali plugin enhances user-friendly conversions of regulatory networks (including reference databases) into signed directed graphs. BioQuali performs automatic global reasoning in order to decide which products in the network need to be up or down regulated (active or inactive) to globally explain experimental data. It highlights incomplete regions in the network, meaning that gene expression levels do not globally correlate with existing knowledge on regulation carried by the topology of the network. Conclusion: The BioQuali plugin facilitates in silico exploration of large-scale regulatory networks by combining the user-friendly tools of the Cytoscape environment with high-performance automatic reasoning algorithms. As a main feature, the plugin guides further investigation regarding a system by highlighting regions in the network that are not accurately described and merit specific study

Doc'UMVF: Two search tools to provide quality-controlled teaching resources in French to students and teachers

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Using Semantic Web Technologies for Clinical Trial Recruitment

International audienceClinical trials are fundamental for medical science: they provide the evaluation for new treatments and new diagnostic approaches. One of the most difficult parts of clinical trials is the recruitment of patients: many trials fail due to lack of participants. Recruitment is done by matching the eligibility criteria of trials to patient conditions. This is usually done manually, but both the large number of active trials and the lack of time available for matching keep the recruitment ratio low. In this paper we present a method, entirely based on standard semantic web technologies and tool, that allows the automatic recruitment of a patient to the available clinical trials. We use a domain specific ontology to represent data from patients' health records and we use SWRL to verify the eligibility of patients to clinical trials

OWL Model of Clinical Trial Eligibility Criteria Compatible With Partially-known Information

Abstract. Clinical trials are important for patients, for researchers and for companies. One of the major bottlenecks is patient recruitment. This task requires to match a great quantity of information about the patient with numerous eligibility criteria, in a logically-complex combination. Moreover, the patient’s information required by some of the eligibility criteria may not be available at the time of pre-screening. In such situations, the classic approach based on negation as failure ignores the distinction between a trial for which patient eligibility should be rejected and trials for which patient eligibility cannot be asserted, which resuls in underestimating recruitment. We propose an OWL design pattern for modeling eligibility criteria based on the open world assumption to address the missing information problem

An Internet supported workflow for the publication process in UMVF (French Virtual Medical University)

International audienc

An Internet supported workflow for the publication process in UMVF (French Virtual Medical University)

International audienc