Automatically extracting information from biomedical text holds the promise of easily consolidating large amounts of biological knowledge in computer-accessible form. This strategy is particularly attractive for extracting data relevant to genes of the human genome from the 11 million abstracts in Medline. However, extraction efforts have been frustrated by the lack of conventions for describing human genes and proteins. We have developed and evaluated a variety of learned information extraction systems for identifying human protein names in Medline abstracts and subsequently extracting information on interactions between the proteins. We demonstrate that machine learning approaches using support vector machines and hidden Markov models are able to identify human proteins with higher accuracy than several previous approaches. We also demonstrate that various rule induction methods are able to identify protein interactions more accurately than manually-developed rules.

PDF, PS

Artificial Intelligence in Medicine (special issue on Summarization and Information Extraction from Medical Documents), 2 (2005), pp. 139-155.

@Article{bunescu:aimed05, title={Comparative Experiments on Learning Information Extractors for Proteins and their Interactions}, author={Razvan Bunescu and Ruifang Ge and Rohit J. Kate and Edward M. Marcotte and Raymond J. Mooney and Arun Kumar Ramani and Yuk Wah Wong}, journal={Artificial Intelligence in Medicine (special issue on Summarization and Information Extraction from Medical Documents)}, number={2}, pages={139-155}, url="http://www.cs.utexas.edu/users/ai-lab?bunescu:aimed05", year={2005} }