50 research outputs found
structured representation of scientific evidence in the biomedical domain using Semantic Web techniques
Background Accounts of evidence are vital to evaluate and reproduce scientific
findings and integrate data on an informed basis. Currently, such accounts are
often inadequate, unstandardized and inaccessible for computational knowledge
engineering even though computational technologies, among them those of the
semantic web, are ever more employed to represent, disseminate and integrate
biomedical data and knowledge. Results We present SEE (Semantic EvidencE), an
RDF/OWL based approach for detailed representation of evidence in terms of the
argumentative structure of the supporting background for claims even in
complex settings. We derive design principles and identify minimal components
for the representation of evidence. We specify the Reasoning and Discourse
Ontology (RDO), an OWL representation of the model of scientific claims, their
subjects, their provenance and their argumentative relations underlying the
SEE approach. We demonstrate the application of SEE and illustrate its design
patterns in a case study by providing an expressive account of the evidence
for certain claims regarding the isolation of the enzyme glutamine synthetase.
Conclusions SEE is suited to provide coherent and computationally accessible
representations of evidence-related information such as the materials,
methods, assumptions, reasoning and information sources used to establish a
scientific finding by adopting a consistently claim-based perspective on
scientific results and their evidence. SEE allows for extensible evidence
representations, in which the level of detail can be adjusted and which can be
extended as needed. It supports representation of arbitrary many consecutive
layers of interpretation and attribution and different evaluations of the same
data. SEE and its underlying model could be a valuable component in a variety
of use cases that require careful representation or examination of evidence
for data presented on the semantic web or in other formats
HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology
We present HepatoNet1, a manually curated large-scale metabolic network of the human hepatocyte that encompasses >2500 reactions in six intracellular and two extracellular compartments.Using constraint-based modeling techniques, the network has been validated to replicate numerous metabolic functions of hepatocytes corresponding to a reference set of diverse physiological liver functions.Taking the detoxification of ammonia and the formation of bile acids as examples, we show how these liver-specific metabolic objectives can be achieved by the variable interplay of various metabolic pathways under varying conditions of nutrients and oxygen availability
Knowledge graphs - working group charter (NFDI section-metadata)
Knowledge Graphs are a key technology for implementing the FAIR principles in data infrastructures by ensuring interoperability for both humans and machines. The Working Group "Knowledge Graphs" in Section "(Meta)data, Terminologies, Provenance" of the German National Research Data Infrastructure (Nationale Forschungsdateninfrastruktur (NFDI) e.V.) aims to promote the use of knowledge graphs in all NFDI consortia, to facilitate cross-domain data interlinking and federation following the FAIR principles, and to contribute to the joint development of tools and technologies that enable transformation of structured and unstructured data into semantically reusable knowledge across different domains
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