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Motivation: The importance of clinical data in understanding the pathophysiology of complex disorders has prompted the launch of multiple initiatives designed to generate patient-level data from various modalities. While these studies can reveal important findings relevant to the disease, each study captures different yet complementary aspects and modalities which, when combined, generate a more comprehensive picture of disease aetiology. However, achieving this requires a global integration of data across studies, which proves to be challenging given the lack of interoperability of cohort datasets. Results: Here, we present the Data Steward Tool (DST), an application that allows for semi-automatic semantic integration of clinical data into ontologies and global data models and data standards. We demonstrate the applicability of the tool in the field of dementia research by establishing a Clinical Data Model (CDM) in this domain. The CDM currently consists of 277 common variables covering demographics (e.g. age and gender), diagnostics, neuropsychological tests, and biomarker measurements. The DST combined with this disease-specific data model shows how interoperability between multiple, heterogeneous dementia datasets can be achieved., Comment: 13 pages, 5 figures, 2 tables

International audience; INTRODUCTION:Multiple immunity biomarkers have been suggested as tracers of neuroinflammation in neurodegeneration. This study aimed to verify findings in cerebrospinal fluid (CSF) samples of Alzheimer's disease (AD) and Parkinson's disease (PD) subjects from the network of the European, Innovative Medicines Initiative-funded project AETIONOMY.METHODS:A total of 227 samples from the studies/centres AETIONOMY, ICEBERG, and IDIBAPS were used to analyse 21 selected immunity biomarkers in CSF. Results were compared to data of an independent cohort of 399 subjects previously published.RESULTS:Immunity markers were predominantly and reproducibly associated with pathological levels of tau isoforms, but also with amyloid levels, aging, sex, APOE genotype, and center-specific factors.DISCUSSION:Immunity biomarker levels in CSF reflect molecular and cellular pathology rather than diagnosis in neurodegenerative disorders. Assay standardization and stratification for age and other covariates could improve the power of such markers in clinical applications or intervention studies targeting immune responses in neurodegeneration.

Motivation The concept of a ‘mechanism-based taxonomy of human disease’ is currently replacing the outdated paradigm of diseases classified by clinical appearance. We have tackled the paradigm of mechanism-based patient subgroup identification in the challenging area of research on neurodegenerative diseases. Results We have developed a knowledge base representing essential pathophysiology mechanisms of neurodegenerative diseases. Together with dedicated algorithms, this knowledge base forms the basis for a ‘mechanism-enrichment server’ that supports the mechanistic interpretation of multiscale, multimodal clinical data. Availability and implementation NeuroMMSig is available at http://neurommsig.scai.fraunhofer.de/ Supplementary information Supplementary data are available at Bioinformatics online.

Background Despite the unprecedented and increasing amount of data, relatively little progress has been made in molecular characterization of mechanisms underlying Parkinson’s disease. In the area of Parkinson’s research, there is a pressing need to integrate various pieces of information into a meaningful context of presumed disease mechanism(s). Disease ontologies provide a novel means for organizing, integrating, and standardizing the knowledge domains specific to disease in a compact, formalized and computer-readable form and serve as a reference for knowledge exchange or systems modeling of disease mechanism. Methods The Parkinson’s disease ontology was built according to the life cycle of ontology building. Structural, functional, and expert evaluation of the ontology was performed to ensure the quality and usability of the ontology. A novelty metric has been introduced to measure the gain of new knowledge using the ontology. Finally, a cause-and-effect model was built around PINK1 and two gene expression studies from the Gene Expression Omnibus database were re-annotated to demonstrate the usability of the ontology. Results The Parkinson’s disease ontology with a subclass-based taxonomic hierarchy covers the broad spectrum of major biomedical concepts from molecular to clinical features of the disease, and also reflects different views on disease features held by molecular biologists, clinicians and drug developers. The current version of the ontology contains 632 concepts, which are organized under nine views. The structural evaluation showed the balanced dispersion of concept classes throughout the ontology. The functional evaluation demonstrated that the ontology-driven literature search could gain novel knowledge not present in the reference Parkinson’s knowledge map. The ontology was able to answer specific questions related to Parkinson’s when evaluated by experts. Finally, the added value of the Parkinson’s disease ontology is demonstrated by ontology-driven modeling of PINK1 and re-annotation of gene expression datasets relevant to Parkinson’s disease. Conclusions Parkinson’s disease ontology delivers the knowledge domain of Parkinson’s disease in a compact, computer-readable form, which can be further edited and enriched by the scientific community and also to be used to construct, represent and automatically extend Parkinson’s-related computable models. A practical version of the Parkinson’s disease ontology for browsing and editing can be publicly accessed at http://bioportal.bioontology.org/ontologies/PDON. Electronic supplementary material The online version of this article (doi:10.1186/s12976-015-0017-y) contains supplementary material, which is available to authorized users.

Running large MPI-applications with resource demands exceeding the local site's cluster capacity could be distributed across a number of clusters in a Grid instead, to satisfy the demand. However, there are a number of drawbacks limiting the applicability of this approach: communication paths between compute nodes of different clusters usually provide lower bandwidth and higher latency than the cluster internal ones, MPI libraries use dedicated I/O-nodes for inter-cluster communication which become a bottleneck, missing tools for co-ordinating the availability of the different clusters across different administrative domains is another issue. To make the Grid approach efficient several prerequisites must be in place: an implementation of MPI providing high-performance communication mechanisms across the borders of clusters, a network connection with high bandwidth and low latency dedicated to the application, compute nodes made available to the application exclusively, and finally a Grid middleware glueing together everything. In this paper we present work recently completed in the VIOLA project: MetaMPICH, user controlled QoS of clusters and interconnecting network, a MetaScheduling Service and the UNICORE integration.