title: A knowledge engineering approach to recognizing and extracting sequences of nucleic acids from scientific literature.
abstract: In this paper we present a knowledge engineering approach to automatically recognize and extract genetic sequences from scientific articles. To carry out this task, we use a preliminary recognizer based on a finite state machine to extract all candidate DNA/RNA sequences. The latter are then fed into a knowledge-based system that automatically discards false positives and refines noisy and incorrectly merged sequences. We created the knowledge base by manually analyzing different manuscripts containing genetic sequences. Our approach was evaluated using a test set of 211 full-text articles in PDF format containing 3134 genetic sequences. For such set, we achieved 87.76% precision and 97.70% recall respectively. This method can facilitate different research tasks. These include text mining, information extraction, and information retrieval research dealing with large collections of documents containing genetic sequences.
authors.affiliation: Biomedical Informatics Group and the Dept. Inteligencia Artificial, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, 28660 Boadilla del Monte, Spain. mgarcia@informed.dia.fi.upm.es
authors.forename: Miguel
authors.initials: M
authors.lastname: García-Remesal
authors.forename: Victor
authors.initials: V
authors.lastname: Maojo
authors.forename: José
authors.initials: J
authors.lastname: Crespo
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001483:Base Sequence; D004247:DNA / Q000235:genetics*; D057225:Data Mining / Q000379:methods*; D008969:Molecular Sequence Data; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D010506:Periodicals as Topic; D017422:Sequence Analysis, DNA / Q000379:methods*
chemical_list: D004247:DNA