Text Mining for Biology: Insights from Leading Scientists
Author Information
Author(s): Russ B Altman, Casey M Bergman, Judith Blake, Christian Blaschke, Aaron Cohen, Frank Gannon, Les Grivell, Udo Hahn, William Hersh, Lars Juhl Jensen, Martin Krallinger, Seán I O'Donoghue, Barend Mons, Manuel C Peitsch, Dietrich Rebholz-Schuhmann, Hagit Shatkay, Alfonso Valencia
Hypothesis
Can text mining improve access to biological knowledge?
Conclusion
Text mining has the potential to enhance access to biological literature and improve the efficiency of data curation.
Supporting Evidence
- Text mining can help link biological databases with literature.
- User interfaces need to be intuitive for different classes of users.
- Integration of text mining tools into larger workflows is essential.
- Community-based annotation can enhance the utility of text mining.
- Future evaluations should broaden the scope of entities beyond genes and proteins.
Takeaway
Scientists believe that text mining can help make biological information easier to find and use, like having a super-smart librarian for research papers.
Methodology
The article collects and summarizes opinions from various leading scientists on the role of text mining in biology.
Limitations
The current text mining tools are not yet fully accessible to end users and require better integration with existing workflows.
Participant Demographics
Contributors include experts from biology, pharmacology, bioinformatics, and computer science.
Digital Object Identifier (DOI)
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