The next generation of literature analysis: integration of genomic analysis into text mining.
Text-mining systems are indispensable tools to reduce the increasing flux of information in scientific literature to topics pertinent to a particular interest in focus. Most of the scientific literature is published as unstructured free text, complicating the development of data processing tools, which rely on structured information. To overcome the problems of free text analysis, structured, hand-curated information derived from literature is integrated in text-mining systems to improve precision and recall. In this paper several text-mining approaches are reviewed and the next step in development of text-mining systems, which is based on a concept of multiple lines of evidence, is described: results from literature analysis are combined with evidence from experiments and genome analysis to improve the accuracy of results and to generate additional knowledge beyond what is known solely from literature.
Scherf
Matthias
M
Genomatix Software GmbH, Landsberger Strasse 6, Munich, D-80339, Germany. scherf@genomatix.de
Epple
Anton
A
Werner
Thomas
T
D000043:Abstracting and Indexing / Q000379:methods; D001185:Artificial Intelligence; D003628:Database Management Systems; D016206:Databases, Bibliographic / Q000639:trends*; D004282:Documentation / Q000379:methods* / Q000639:trends; D005544:Forecasting; D023281:Genomics / Q000379:methods* / Q000639:trends*; D016247:Information Storage and Retrieval / Q000379:methods* / Q000639:trends*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D010506:Periodicals as Topic; D018511:Systems Integration; D009626:Terminology as Topic