An entity tagger for recognizing acquired genomic variations in cancer literature | Zendy

Ryan McDonald | Zendy; R. Scott Winters | Zendy; Mark A. Mandel | Zendy; Yang Jin | Zendy; Peter S. White | Zendy; Fernando Pereira | Zendy

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An entity tagger for recognizing acquired genomic variations in cancer literature

Author(s) -

Ryan McDonald,

R. Scott Winters,

Mark A. Mandel,

Yang Jin,

Peter S. White,

Fernando Pereira

Publication year - 2004

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bth350

Subject(s) - computer science , natural language processing , artificial intelligence , software , computational biology , pattern recognition (psychology) , biology , programming language

VTag is an application for identifying the type, genomic location and genomic state-change of acquired genomic aberrations described in text. The application uses a machine learning technique called conditional random fields. VTag was tested with 345 training and 200 evaluation documents pertaining to cancer genetics. Our experiments resulted in 0.8541 precision, 0.7870 recall and 0.8192 F-measure on the evaluation set.

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