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Research using health data is challenged by its heterogeneous nature, description and storage. The COVID-19 outbreak made clear that rapid analysis of observations such as clinical measurements across a large number of healthcare providers can have enormous health benefits. This has brought into focus the need for a common model of quantitative health data that enables data exchange and federated computational analysis. The application of ontologies, Semantic Web technologies and the FAIR principles is an approach used by different life science research projects, such as the European Joint Programme on Rare Diseases, to make data and metadata machine readable and thereby reduce the barriers for data sharing and analytics and harness health data for discovery. Here, we show the reuse of a pattern for measurements to model diverse health data, to demonstrate and raise visibility of the usefulness of this pattern for biomedical research.

A challenge in computational legal research is the quantitative assessment of “relevance” in a network of court decisions. The term “sleeping beauty” (SB) was coined to denote an article that received almost no attention immediately after publication, but suddenly received multiple citations many years later. These publications can be identified by calculating their Beauty coefficient (B-coefficient). In this contribution, we apply approaches used for identifying SBs to decisions arising from the Court of Justice of the European Union (CJEU). We compared B-coefficients of CJEU cases with their centrality scores from classical algorithms from network analysis, finding that these measures tend to correlate. We discuss the implications of this that are interesting for legal scholars, acknowledging that future work is required to calibrate the scale of the time variable in the B-coefficient formula for finer-grained application to case law. Our study’s setup provides a foundation for new case law analytics methodologies that extends the power of traditional network analysis techniques for answering questions about the behavior of European courts.

Validating RDF data becomes necessary in order to ensure data compliance against the conceptualization model it follows, e.g., schema or ontology behind the data, and improve data consistency and completeness. There are different approaches to validate RDF data, for instance, JSON schema, particularly for data in JSONLD format, as well as Shape Expression and Shapes Constraint Language, which can be used with other serialization as well, e.g., RDF/XML or Turtle. Currently, no validation approach is prevalent regarding others, selection commonly depends on data characteristics, background knowledge and personal preferences . In some cases, the approaches are interchangeable; however, that is not always the case, making it necessary to identify a subset among them that can be seamlessly translated from one to another. During the NBDC/DBCLS 2019 BioHackathon, we worked on a variety of topics related to RDF data validation, including (i) development of ShEx shapes for a number of datasets, (ii) development of a tool to semi-automatically create ShEx shapes, (iii) improvements to the RDFShape tool, and (iv) enabling validation schema conversion from one format to the other. Here we report on our BioHackathon achievements.

This chapter introduces the rational and regulatory mechanism underlying the EU data protection framework with specific focus on the EU’s General Data Protection Regulation (GDPR). It outlines the applicability of the research exemption included in the GDPR and discusses further or secondary use of personal data for research purposes.

The Center for Expanded Data Annotation and Retrieval is studying the creation of comprehensive and expressive metadata for biomedical datasets to facilitate data discovery, data interpretation, and data reuse. We take advantage of emerging community-based standard templates for describing different kinds of biomedical datasets, and we investigate the use of computational techniques to help investigators to assemble templates and to fill in their values. We are creating a repository of metadata from which we plan to identify metadata patterns that will drive predictive data entry when filling in metadata templates. The metadata repository not only will capture annotations specified when experimental datasets are initially created, but also will incorporate links to the published literature, including secondary analyses and possible refinements or retractions of experimental interpretations. By working initially with the Human Immunology Project Consortium and the developers of the ImmPort data repository, we are developing and evaluating an end-to-end solution to the problems of metadata authoring and management that will generalize to other data-management environments. ? The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

We present RDFox—a main-memory, scalable, centralised RDF store that supports materialisation-based parallel datalog reasoning and SPARQL query answering. RDFox uses novel and highlyefficient parallel reasoning algorithms for the computation and incremental update of datalog materialisations with efficient handling of owl: sameAs. In this system description paper, we present an overview of the system architecture and highlight the main ideas behind our indexing data structures and our novel reasoning algorithms. In addition, we evaluate RDFox on a high-end SPARC T5-8 server with 128 physical cores and 4TB of RAM. Our results show that RDFox can effectively exploit such a machine, achieving speedups of up to 87 times, storage of up to 9.2 billion triples, memory usage as low as 36.9 bytes per triple, importation rates of up to 1 million triples per second, and reasoning rates of up to 6.1 million triples per second.

Introduction theoretical background experimental approaches for inhibitor discovery, design, and development experimental elucidation of ligand and inhibitor activity rational ligand and inhibitor design conclusions references.

Smart City systems capture and exchange information with the aim to improve public services. Particularly, healthcare data could help emergency services to plan resources and make life-saving decisions. However, the delivery of healthcare information to emergency bodies must be balanced against the concerns related to citizens’ privacy. Besides, emergency services face challenges in interpreting this data; the heterogeneity of sources and a large amount of information available represent a significant barrier. In this paper, we focus on a case study involving the use of personal health records to support emergency services in the context of a fire building evacuation. We propose a methodology involving a knowledge engineering approach and a common-sense knowledge base to address the problem of deriving useful information from health records and, at the same time, preserve citizens’ privacy. We perform extensive experiments involving a synthetic dataset of health records and a curated gold standard to demonstrate how our approach allows us to identify vulnerable people and interpret their particular needs while avoiding the disclosure of personal information.

This study investigated difficulties in the comprehension of SPARQL. In particular, it compared the declarative and navigational styles present in the language, and various operators used in SPARQL property paths. The study involved participants selecting possible answers given a SPARQL query and knowledgebase. In general, no significant differences were found in terms of the response time and accuracy with which participants could answer questions expressed in either a declarative or navigational form. However, UNION did take significantly longer to comprehend than both braces and verti- cal line in property paths; with braces being faster than vertical line. Inversion and negated property paths both proved difficult, with their combination being very difficult indeed. Questions involving MINUS were answered more accu- rately than those involving negation in property paths, in particular where pred- icates were inverted. Both involve negation, but the semantics are different. With the MINUS questions, negation and inversion can be considered separate- ly; with property paths, negation and inversion need to be considered together. Participants generally expressed a preference for data represented graphically, and this preference was significantly correlated with accuracy of comprehen- sion. Implications for the design and use of query languages are discussed.

Item does not contain fulltext Pre-requisites to better understand the chapter: basic knowledge of major sources of clinical data. Logical position of the chapter with respect to the previous chapter: in the previous chapter, you have learned what the major sources of clinical data are. In this chapter, we will dive into the main characteristics of presented data sources. In particular, we will learn how to distinguish and classify data according to its scale. Learning objectives: you will learn the major differences between data sources presented in previous chapters; how clinical data can be classified according to its scale. You will get familiar with the concept of 'big' clinical data; you will learn which are the major concerns limiting 'big' data exchange.

Item does not contain fulltext In the previous chapter, you have learned how to prepare your data before you start the process of generating a predictive model. In this chapter, you will learn how to make a predictive model using very common regression techniques and how to evaluate the performance of a model. In the next chapter we will then look at more advanced machine learning techniques that have become increasingly popular in recent years.

Item does not contain fulltext This is the first chapter of five that cover an introduction to developing and validating models for predicting outcomes for the individual patient. Such prediction models can be used for predicting the occurrence or recurrence of an event, or of the most likely value on a continuous outcome. We will mainly focus on the prediction of binary outcomes, such as the occurrence of a complication, recurrence of disease, the presence of metastases, remission, survival, etc. This chapter deals with the selection of an appropriate study design for a study on prediction, and on methods to manipulate the data before the statistical modelling can begin.

Item does not contain fulltext Prediction models have the potential to positively influence clinical decision-making and thus the overall quality of healthcare. The translational gap needs to be bridged between development of complex statistical models requiring multiple predictors and widespread usage in clinical consultation. A recent review found that inadequate quality of reporting of prediction modelling studies could be a contributing factor in slow transition to the clinic. This chapter emphasises the importance of high-quality reporting of modelling studies and the need for critical appraisal to understand the potential issues limiting generalizability of published models. Evidence synthesis (such as systematic reviews and pooled analysis of disparate models) are relatively under-represented in literature, though methodological studies and guidelines are now starting to appear.