A call for paper in

A large part of the information required for biology research can only be found in free-text form, as in MEDLINE abstracts, or in comment fields of relevant reports, as in GenBank feature table annotations. This information is important for many types of analysis, such as classification of proteins into functional groups, discovery of new functional relationships, maintenance of  information on material and methods, increased  precision and relevance of hits returned by BLAST, extraction of protein interaction information, and so on. However, information in free-text form or in comment fields is very difficult for automated systems to use. In addition, the extracted information  may need further enrichment, for example, through the inclusion of quantitative information about the interaction. This session will investigate how natural language and data mining techniques can provide and structure information relevant to biological applications. The session solicits papers on techniques and applications of natural language processing to the extraction of biological information from free text, including literature abstracts (e.g., MEDLINE), database annotations (e.g., GENBANK or PIR), and other relevant biology sources. It will emphasize the combination of natural language techniques with other biological information sources, such as database and sequence searches, to facilitate collection and organization of information about particular genes, proteins, or pathways. In particular, we are interested in:

• Novel ways of combining text data mining and more conventional bioinformatics search techniques;
• Use of text data mining techniques for consistency checking and error detection in annotation of existing data bases;
• Biological problems where extraction of text-based information can provide quantitative performance gains;
• Evaluations of the utility of text data mining techniques and components;
• Extraction and organization of text-based information facilitated by ontologies and data exchange standards;
• Creation of structured resources (databases) through the use of text data mining and information extraction techniques.

Session co-chairs

• Lynette Hirschman, MITRE

lynette@mitre.org
• Jong C. Park, KAIST

park@nlp.kaist.ac.kr
• Junichi Tsujii, University of Tokyo

tsujii@is.s.u-tokyo.ac.jp
• Limsoon Wong, KRDL

limsoon@krdl.org.sg
• Cathy Wu, National Biomedical Research Foundation & Georgetown University

wuc@nbrf.georgetown.edu

Submission information

All papers must be submitted to russ.altman@stanford.edu in electronic format. The file formats we accept are: postscript (*.ps), adobe acrobat (*.pdf) and Microsoft Word documents (*.doc). Attached files should be named with the last name of the first author (e.g. altman.ps, altman.pdf, or altman.doc). Hardcopy submissions or unprocessed TEX or LATEX files will be rejected without review.

Each paper must be accompanied by a cover letter. The cover letter must state the following:

• The email address of the corresponding author
• The specific PSB session that should review the paper or abstract
• The submitted paper contains original, unpublished results, and is not currently under consideration elsewhere.
• All co-authors concur with the contents of the paper.