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, wh...

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Bibliographic Details
Published in:Briefings in bioinformatics 2005-09, Vol.6 (3), p.287-297
Main Authors: SCHERF, M, EPPLE, A, WERNER, T
Format: Article
Language:English
Subjects:
Abstracting and Indexing as Topic - methods
Artificial Intelligence
Biological and medical sciences
Data analysis
Data collection
Database Management Systems
Databases, Bibliographic - trends
Documentation - methods
Documentation - trends
Forecasting
Fundamental and applied biological sciences. Psychology
General aspects
Genetic research
Genomics
Genomics - methods
Genomics - trends
Information management
Information Storage and Retrieval - methods
Information Storage and Retrieval - trends
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Natural Language Processing
Pattern Recognition, Automated - methods
Periodicals as Topic
Systems Integration
Terminology as Topic
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Summary: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.
ISSN:1467-5463
1477-4054
DOI:10.1093/bib/6.3.287