Mining the biomedical literature using semantic analysis and natural language processing techniques |
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| Affiliation: | 1. Dept of Computer Science, Bar-Ilan University, Ramat-Gan, Israel 52900 tel: +972 3 7350000 fax: +972 3 7350001;2. ClearForest Corporation, Or Yehuda, Israel 60376;1. National Centre for Biological Sciences, TIFR, UAS-GKVK Campus, Bellary Road, Bangalore 560065, India;2. Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India;3. Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck College, University of London, Malet Street, London WC1E 7HX, UK;4. Molecular Structure and Function Program, Hospital for Sick Children, Toronto, Canada;1. Rehabilitation Science Institute, University of Toronto, Toronto, Ontario, Canada;2. Mobility Research Team, Toronto Rehabilitation Institute, Toronto, Ontario, Canada;3. Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada;4. Canadian Partnership for Stroke Recovery, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada;1. Department of Chemistry, Faculty of Science, Kasetsart University, 50 Ngam Wong Wan Rd., Chatuchak, Bangkok 10900, Thailand;2. Department of Social and Applied Science, College of Industrial Technology, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand |
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| Abstract: | The information age has made the electronic storage of large amounts of data effortless. The proliferation of documents available on the Internet, corporate intranets, news wires and elsewhere is overwhelming. Search engines only exacerbate this overload problem by making increasingly more documents available in only a few keystrokes. This information overload also exists in the biomedical field, where scientific publications, and other forms of text-based data are produced at an unprecedented rate. Text mining is the combined, automated process of analyzing unstructured, natural language text to discover information and knowledge that are typically difficult to retrieve. Here, we focus on text mining as applied to the biomedical literature. We focus in particular on finding relationships among genes, proteins, drugs and diseases, to facilitate an understanding and prediction of complex biological processes. The LitMiner™ system, developed specifically for this purpose; is described in relation to the Knowledge Discovery and Data Mining Cup 2002, which serves as a formal evaluation of the system. |
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