Linked Registries: Connecting Rare Diseases Patient Registries through a Semantic Web Layer

Patient registries are an essential tool to increase current knowledge regarding rare diseases. Understanding these data is a vital step to improve patient treatments and to create the most adequate tools for personalized medicine. However, the growing number of disease-specific patient registries brings also new technical challenges. Usually, these systems are developed as closed data silos, with independent formats and models, lacking comprehensive mechanisms to enable data sharing. To tackle these challenges, we developed a Semantic Web based solution that allows connecting distributed and heterogeneous registries, enabling the federation of knowledge between multiple independent environments. This semantic layer creates a holistic view over a set of anonymised registries, supporting semantic data representation, integrated access, and querying. The implemented system gave us the opportunity to answer challenging questions across disperse rare disease patient registries. The interconnection between those registries using Semantic Web technologies benefits our final solution in a way that we can query single or multiple instances according to our needs. The outcome is a unique semantic layer, connecting miscellaneous registries and delivering a lightweight holistic perspective over the wealth of knowledge stemming from linked rare disease patient registries

Sustainable Economic Growth under Declining Population

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A novel chemogenomics analysis of G protein-coupled receptors (GPCRs) and their ligands: a potential strategy for receptor de-orphanization.

BACKGROUND: G protein-coupled receptors (GPCRs) represent a family of well-characterized drug targets with significant therapeutic value. Phylogenetic classifications may help to understand the characteristics of individual GPCRs and their subtypes. Previous phylogenetic classifications were all based on the sequences of receptors, adding only minor information about the ligand binding properties of the receptors. In this work, we compare a sequence-based classification of receptors to a ligand-based classification of the same group of receptors, and evaluate the potential to use sequence relatedness as a predictor for ligand interactions thus aiding the quest for ligands of orphan receptors. RESULTS: We present a classification of GPCRs that is purely based on their ligands, complementing sequence-based phylogenetic classifications of these receptors. Targets were hierarchically classified into phylogenetic trees, for both sequence space and ligand (substructure) space. The overall organization of the sequence-based tree and substructure-based tree was similar; in particular, the adenosine receptors cluster together as well as most peptide receptor subtypes (e.g. opioid, somatostatin) and adrenoceptor subtypes. In ligand space, the prostanoid and cannabinoid receptors are more distant from the other targets, whereas the tachykinin receptors, the oxytocin receptor, and serotonin receptors are closer to the other targets, which is indicative for ligand promiscuity. In 93% of the receptors studied, de-orphanization of a simulated orphan receptor using the ligands of related receptors performed better than random (AUC > 0.5) and for 35% of receptors de-orphanization performance was good (AUC > 0.7). CONCLUSIONS: We constructed a phylogenetic classification of GPCRs that is solely based on the ligands of these receptors. The similarities and differences with traditional sequence-based classifications were investigated: our ligand-based classification uncovers relationships among GPCRs that are not apparent from the sequence-based classification. This will shed light on potential cross-reactivity of GPCR ligands and will aid the design of new ligands with the desired activity profiles. In addition, we linked the ligand-based classification with a ligand-focused sequence-based classification described in literature and proved the potential of this method for de-orphanization of GPCRs.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

The implicitome: A resource for rationalizing gene-disease associations

High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing

Managing food and microbiome studies data using Fairspace, a flexible and FAIR data management platform

Fairspace is an open-source research data management platform that adheres to the FAIR principles. The Hyve created Fairspace in 2016 and has developed it ever since, customizing it for several organizations' use cases. We present the implementation of this tool within the FNS-Cloud consortium, in which Fairspace became the user browser that allows microbiome and food data exploration within public resources mapped to a common (meta)data model and vocabularies/ontologies

New suite of Concept Profile Analysis Web Services

<p>This zipfile contains the source code for the Concept Profile Mining Web services.<br> The directory 'erasmusmc_maven_dependencies' contains copies of the erasmus-mc jar files that are hosted at the DTL nexus repository.</p> <p> </p

Multi-Objective Evolutionary Design of Adenosine Receptor Ligands

A novel multiobjective evolutionary algorithm (MOEA) for <i>de novo</i> design was developed and applied to the discovery of new adenosine receptor antagonists. This method consists of several iterative cycles of structure generation, evaluation, and selection. We applied an evolutionary algorithm (the so-called Molecule Commander) to generate candidate A₁ adenosine receptor antagonists, which were evaluated against multiple criteria and objectives consisting of high (predicted) affinity and selectivity for the receptor, together with good ADMET properties. A pharmacophore model for the human A₁ adenosine receptor (hA₁AR) was created to serve as an objective function for evolution. In addition, three support vector machine models based on molecular fingerprints were developed for the other adenosine receptor subtypes (hA_2A, hA_2B, and hA₃) and applied as negative objective functions, to aim for selectivity. Structures with a higher evolutionary fitness with respect to ADMET and pharmacophore matching scores were selected as input for the next generation and thus developed toward overall fitter (“better”) compounds. We finally obtained a collection of 3946 unique compounds from which we derived chemical scaffolds. As a proof-of-principle, six of these templates were selected for actual synthesis and subsequently tested for activity toward all adenosine receptors subtypes. Interestingly, scaffolds <b>2</b> and <b>3</b> displayed low micromolar affinity for many of the adenosine receptor subtypes. To further investigate our evolutionary design method, we performed systematic modifications on scaffold <b>3</b>. These modifications were guided by the substitution patterns as observed in the set of generated compounds that contained scaffold <b>3</b>. We found that an increased affinity with appreciable selectivity for hA₁AR over the other adenosine receptor subtypes was achieved through substitution of the scaffold; compound <b>3a</b> had a <i>K</i>_<i>i</i> value of 280 nM with approximately 10-fold selectivity with respect to hA_2AR, while <b>3g</b> had a 1.6 μM affinity for hA₁AR with negligible affinity for the hA_2A, hA_2B, and hA₃ receptor subtypes