Matches in UGent Biblio for { <https://biblio.ugent.be/publication/2974138#aggregation> ?p ?o. }
Showing items 1 to 34 of
34
with 100 items per page.
- aggregation classification "A1".
- aggregation creator B284402.
- aggregation creator B284403.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2012".
- aggregation format "application/pdf".
- aggregation hasFormat 2974138.bibtex.
- aggregation hasFormat 2974138.csv.
- aggregation hasFormat 2974138.dc.
- aggregation hasFormat 2974138.didl.
- aggregation hasFormat 2974138.doc.
- aggregation hasFormat 2974138.json.
- aggregation hasFormat 2974138.mets.
- aggregation hasFormat 2974138.mods.
- aggregation hasFormat 2974138.rdf.
- aggregation hasFormat 2974138.ris.
- aggregation hasFormat 2974138.txt.
- aggregation hasFormat 2974138.xls.
- aggregation hasFormat 2974138.yaml.
- aggregation isPartOf urn:issn:1471-2105.
- aggregation language "eng".
- aggregation rights "I have retained and own the full copyright for this publication".
- aggregation subject "Biology and Life Sciences".
- aggregation title "Semantically linking molecular entities in literature through entity relationships".
- aggregation abstract "Background: Text mining tools have gained popularity to process the vast amount of available research articles in the biomedical literature. It is crucial that such tools extract information with a sufficient level of detail to be applicable in real life scenarios. Studies of mining non-causal molecular relations attribute to this goal by formally identifying the relations between genes, promoters, complexes and various other molecular entities found in text. More importantly, these studies help to enhance integration of text mining results with database facts. Results: We describe, compare and evaluate two frameworks developed for the prediction of non-causal or 'entity' relations (REL) between gene symbols and domain terms. For the corresponding REL challenge of the BioNLP Shared Task of 2011, these systems ranked first (57.7% F-score) and second (41.6% F-score). In this paper, we investigate the performance discrepancy of 16 percentage points by benchmarking on a related and more extensive dataset, analysing the contribution of both the term detection and relation extraction modules. We further construct a hybrid system combining the two frameworks and experiment with intersection and union combinations, achieving respectively high-precision and high-recall results. Finally, we highlight extremely high-performance results (F-score >90%) obtained for the specific subclass of embedded entity relations that are essential for integrating text mining predictions with database facts. Conclusions: The results from this study will enable us in the near future to annotate semantic relations between molecular entities in the entire scientific literature available through PubMed. The recent release of the EVEX dataset, containing biomolecular event predictions for millions of PubMed articles, is an interesting and exciting opportunity to overlay these entity relations with event predictions on a literature-wide scale.".
- aggregation authorList BK564539.
- aggregation volume "13".
- aggregation aggregates 3207063.
- aggregation isDescribedBy 2974138.
- aggregation similarTo 1471-2105-13-S11-S6.
- aggregation similarTo LU-2974138.