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10.1186/s13326-017-0132-2 http://scihub22266oqcxt.onion/10.1186/s13326-017-0132-2 C5477351!5477351!28629436     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28629436&cmd=llinks): Failed to open stream: HTTP request failed! 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Disease Compass? a navigation system for disease knowledge based on ontology and linked data techniques |pdf=|usr=}}{{28629436}} gab.com Text Disease Compass a navigation system for disease knowledge based on ontology and linked data techniques JO: J Biomed Semantics http://www.kidney.de/pget.php?&tw=1&pmid=28629436 #FOAvip Background: Medical ontologies are expected to contribute to the effective use of medical information resources that store considerable amount of data. In this study, we focused on disease ontology because the complicated mechanisms of diseases are related to concepts across various medical domains. The authors developed a River Flow Model (RFM) of diseases, which captures diseases as the causal chains of abnormal states. It represents causes of diseases, disease progression, and downstream consequences of diseases, which is compliant with the intuition of medical experts. In this paper, we discuss a fact repository for causal chains of disease based on the disease ontology. It could be a valuable knowledge base for advanced medical information systems. Methods: We developed the fact repository for causal chains of diseases based on our disease ontology and abnormality ontology. This section summarizes these two ontologies. It is developed as linked data so that information scientists can access it using SPARQL queries through an Resource Description Framework (RDF) model for causal chain of diseases. Results: We designed the RDF model as an implementation of the RFM for the fact repository based on the ontological definitions of the RFM. 1554 diseases and 7080 abnormal states in six major clinical areas, which are extracted from the disease ontology, are published as linked data (RDF) with SPARQL endpoint (accessible API). Furthermore, the authors developed Disease Compass, a navigation system for disease knowledge. Disease Compass can browse the causal chains of a disease and obtain related information, including abnormal states, through two web services that provide general information from linked data, such as DBpedia, and 3D anatomical images. Conclusions: Disease Compass can provide a complete picture of disease-associated processes in such a way that fits with a clinician?s understanding of diseases. Therefore, it supports user exploration of disease knowledge with access to pertinent information from a variety of sources. Electronic supplementary material: The online version of this article (doi:10.1186/s13326-017-0132-2) contains supplementary material, which is available to authorized users. Twit Text FOAVip Disease Compass a navigation system for disease knowledge based on ontology and linked data techniques ????J Biomed Semantics http://www.kidney.de/pget.php?&tw=1&pmid=28629436 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28629436 #FOAvip English Wikipedia <ref>{{Cite pmid|28629436}}</ref> Disease Compass? a navigation system for disease knowledge based on ontology and linked data techniques #MMPMID28629436 Kozaki K; Yamagata Y; Mizoguchi R; Imai T; Ohe K J Biomed Semantics 2017[]; 8 (ä): ä PMID28629436show ga Background: Medical ontologies are expected to contribute to the effective use of medical information resources that store considerable amount of data. In this study, we focused on disease ontology because the complicated mechanisms of diseases are related to concepts across various medical domains. The authors developed a River Flow Model (RFM) of diseases, which captures diseases as the causal chains of abnormal states. It represents causes of diseases, disease progression, and downstream consequences of diseases, which is compliant with the intuition of medical experts. In this paper, we discuss a fact repository for causal chains of disease based on the disease ontology. It could be a valuable knowledge base for advanced medical information systems. Methods: We developed the fact repository for causal chains of diseases based on our disease ontology and abnormality ontology. This section summarizes these two ontologies. It is developed as linked data so that information scientists can access it using SPARQL queries through an Resource Description Framework (RDF) model for causal chain of diseases. Results: We designed the RDF model as an implementation of the RFM for the fact repository based on the ontological definitions of the RFM. 1554 diseases and 7080 abnormal states in six major clinical areas, which are extracted from the disease ontology, are published as linked data (RDF) with SPARQL endpoint (accessible API). Furthermore, the authors developed Disease Compass, a navigation system for disease knowledge. Disease Compass can browse the causal chains of a disease and obtain related information, including abnormal states, through two web services that provide general information from linked data, such as DBpedia, and 3D anatomical images. Conclusions: Disease Compass can provide a complete picture of disease-associated processes in such a way that fits with a clinician?s understanding of diseases. Therefore, it supports user exploration of disease knowledge with access to pertinent information from a variety of sources. Electronic supplementary material: The online version of this article (doi:10.1186/s13326-017-0132-2) contains supplementary material, which is available to authorized users. äDisease Compass? a navigation system for disease knowledge based on on(epmc).pdf DeepDyve Pubget Overpricing