Michael Krauthammer
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Michael Krauthammer is a person.
- AKA: M. Krauthammer.
- See: Biomedical Literature, Text Mining.
References
- http://krauthammerlab.med.yale.edu/members/michael-krauthammer
- DBLP Author Page: http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/k/Krauthammer:Michael.html
- http://scholar.google.com/scholar?q=%22Michael+Krauthammer%22
2008
- (Leitner et al., 2008) ⇒ Florian Leitner, Martin Krallinger, Carlos Rodriguez-Penagos, Jörg Hakenberg, Conrad Plake, Cheng-Ju Kuo, Chun-Nan Hsu,Richard Tzong-Han Tsai, Hsi-Chuan Hung William W Lau, Calvin A Johnson, Rune Sætre, Kazuhiro Yoshida, Yan Hua Chen, Sun Kim, Soo-Yong Shin, Byoung-Tak Zhang, William A. Baumgartner Jr, Lawrence Hunter, Barry Haddow, Michael Matthews, Xinglong Wang, Patrick Ruch, Frédéric Ehrler, Arzucan Özgür, Güneş Erkan, Dragomir Radev, Michael Krauthammer, ThaiBinh Luong, Robert Hoffmann, Chris Sander, and Alfonso Valencia. (2008). “Introducing Meta-Services for Biomedical Information Extraction.” In: Genome Biology 2008, 9(Suppl 2):S6
2007
- (Luong et al., 2007) ⇒ T. Luong, N. Tran, and Michael Krauthammer. (2007). “Context-aware Mapping of Gene Names Using Trigrams.” In: Proceedings of the Second BioCreative Challenge Workshop.
2004
- (Krauthammer & Nenadic, 2004) ⇒ Michael Krauthammer, and Goran Nenadic. (2004). “Term Identification in the Biomedical Literature.” In: Journal of Biomedical Informatics, 37(6). doi:10.1016/j.jbi.2004.08.004
2001
- (Friedman, Kra et al., 2001) ⇒ Carol Friedman, Pauline Kra, Hong Yu, Michael Krauthammer, and Andrey Rzhetsky. (2001). “GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles.” In: Bioinformatics, 17 (suppl 1). doi:10.1093/bioinformatics/17.suppl_1.S74
- ABSTRACT: Systems that extract structured information from natural language passages have been highly successful in specialized domains. The time is opportune for developing analogous applications for molecular biology and genomics. We present a system, GENIES, that extracts and structures information about cellular pathways from the biological literature in accordance with a knowledge model that we developed earlier. We implemented GENIES by modifying an existing medical natural language processing system, MedLEE, and performed a preliminary evaluation study. Our results demonstrate the value of the underlying techniques for the purpose of acquiring valuable knowledge from biological journals.