Hairpins in bookstacks: Information retrieval from biomedical text
Associate professor at the School of Computing, Queen's University. She works in the area of machine learning and its application to biomedical data. She is an active member of the biomedical text-mining community, and one of the first researchers in the area of text mining and information retrieval for bioinformatics.
Hagit Shatkay, School of Computing, Queen's University, Kingston, Ontario, K7L 3N6, Canada Tel: +1 613 533 6426 Fax: +1 613 533 6513 E-mail: shatkay{at}cs.queensu.ca
Current advances in high-throughput biology are accompanied by a tremendous increase in the number of related publications. Much biomedical information is reported in the vast amount of literature. The ability to rapidly and effectively survey the literature is necessary for both the design and the interpretation of large-scale experiments, and for curation of structured biomedical knowledge in public databases. Given the millions of published documents, the field of information retrieval, which is concerned with the automatic identification of relevant documents from large text collections, has much to offer. This paper introduces the basics of information retrieval, discusses its applications in biomedicine, and presents traditional and non-traditional ways in which it can be used.
Keywords: information retrieval, text mining, biomedical text mining, biomedical literature mining, information retrieval, NLP
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