literature based discovery
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literature based discovery in biomedical literature using ontologies and other aids
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literature based discovery
- 13. age approximately average cardiovascular characteristics comorbid conditions disease example high average age of participants approximately 63 years predominance of women high prevalence comorbid conditions
- 17. In order to support Literature Based Discovery Ontologies Communities Annotation Machine-readable documents In a nutshell…. …documents as interfaces to the Web of Data…. Biotea • Machine-readable and procesable documents • Interactive documents • Enriched metadata • Full content management, document centric • Social hub Citagora -Aggregated search -Single entry point -Social hub -Citation centric
- 19. RDFReacto r PMC RDFication PMC XML RDF Generation BIBO References Enrichment Metadata+ Content + References
- 20. RDF4PMC, some results Makes possible How similar are two articles? based on authors, keywords, abstracts, ontological terms What articles use this reference in a section with title “Results”? Annotations Metadata + Content + References Annotations Makes possible • How similar are two articles? based on semantic distance • Which annotation co-occurs more with this “YYY” annotation? • Which articles include “TERM” but not this other “TERM”? Some numbers, article PMC126253 “Computational method for reducing variance with Affymetrix microarrays” • NCBO • Annotations: 407 • Topics: 633 • Whatizit • Annotations: 14 • Topics: 203 Delivering: the platform that makes possible to build interactive environments for semantic publications
- 21. Title & authors Links Abstra ct Cloud of Bio- annotations (term + # of bio- entities) Paragraphs containing the annotation selected by the user
- 22. Enriched content: interactive zone for the bio-entity selected by user Bio-entities for the annotation selected
Editor's Notes
- This is an overview of the ontologies, APIs, and web services used in our rdfication process. Initially we take the XML provided by PMC for those open access articles. We use BIBO, Dublin Core, FOAF, and DOCO to model the paper. We used the RDFReactor tool in order to get the JAVA classes corresponding to BIBO and DOCO, it was not necessary for Dublin Core and FOAF as we do not used them as extensevely as BIBO and DOCO. Particularly for the references, when the information provided in the XML is incomplete or difficult to process, it is possible to use services such as those provided by NCBI, Mendeley, crossref.org, the DataCite Metadata Schema Repository, etc., so we can improve the metadata for the references. That is currently part of our future work. We also add links to the pubmed space in Bio2RDF and identifiers.org. A similar process could be done for other publishers
- Now, let’s see an example consuming the RDF we have generate so far. In this prototype we are focusing on human genes, particualrly those cover by GeneWiki. We first retrieve the UniProt accession corresponding to the gene name, then we look for publications annotated with this accession. This is a first prototype so we are not yet covering synonyms or related terms, that is part of our future work. The publication is displayed in an enriched environment that includes different functionalities depending on the type of the biological entity selected by the user, so far we have included genes, proteins, and chemical entities.