Matches in UGent Biblio for { <https://biblio.ugent.be/publication/2093782#aggregation> ?p ?o. }
Showing items 1 to 33 of
33
with 100 items per page.
- aggregation classification "C1".
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2010".
- aggregation format "application/pdf".
- aggregation hasFormat 2093782.bibtex.
- aggregation hasFormat 2093782.csv.
- aggregation hasFormat 2093782.dc.
- aggregation hasFormat 2093782.didl.
- aggregation hasFormat 2093782.doc.
- aggregation hasFormat 2093782.json.
- aggregation hasFormat 2093782.mets.
- aggregation hasFormat 2093782.mods.
- aggregation hasFormat 2093782.rdf.
- aggregation hasFormat 2093782.ris.
- aggregation hasFormat 2093782.txt.
- aggregation hasFormat 2093782.xls.
- aggregation hasFormat 2093782.yaml.
- aggregation isPartOf urn:issn:1938-7288.
- aggregation language "eng".
- aggregation publisher "Microtome Publishing".
- aggregation rights "I have retained and own the full copyright for this publication".
- aggregation subject "Science General".
- aggregation title "Event based text mining for integrated network construction".
- aggregation abstract "The scientific literature is a rich and challenging data source for research in systems biology, providing numerous interactions between biological entities. Text mining techniques have been increasingly useful to extract such information from the literature in an automatic way, but up to now the main focus of text mining in the systems biology field has been restricted mostly to the discovery of protein-protein interactions. Here, we take this approach one step further, and use machine learning techniques combined with text mining to extract a much wider variety of interactions between biological entities. Each particular interaction type gives rise to a separate network, represented as a graph, all of which can be subsequently combined to yield a so-called integrated network representation. This provides a much broader view on the biological system as a whole, which can then be used in further investigations to analyse specific properties of the network".
- aggregation authorList BK312639.
- aggregation endPage "121".
- aggregation startPage "112".
- aggregation volume "8".
- aggregation aggregates 2990082.
- aggregation isDescribedBy 2093782.
- aggregation similarTo LU-2093782.