Large-scale extraction of gene regulation for model organisms in an ontological context.
This paper presents an approach using syntactosemantic rules for the extraction of relational information from biomedical abstracts. The results show that by overcoming the hurdle of technical terminology, high precision results can be achieved. From abstracts related to baker's yeast, we manage to extract a regulatory network comprised of 441 pairwise relations from 58,664 abstracts with an accuracy of 83 - 90%. To achieve this, we made use of a resource of gene/protein names considerably larger than those used in most other biology related information extraction approaches. This list of names was included in the lexicon of our retrained partof- speech tagger for use on molecular biology abstracts. For the domain in question an accuracy of 93.6 - 97.7% was attained on Part-of-speech-tags. The method can be easily adapted to other organisms than yeast, allowing us to extract many more biologically relevant relations. The main reason for the comparable precision rates is the ontological model that was built beforehand and served as a guiding force for the manual coding of the syntactosemantic rules.
Saric
Jasmin
J
European Media Laboratory GmbH, D-69118 Heidelberg, Germany. saric@eml-r.org
Jensen
Lars J
LJ
Rojas
Isabel
I
D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology; D005656:Fungal Proteins / Q000096:biosynthesis; D015966:Gene Expression Regulation, Fungal; D005800:Genes, Fungal; D016247:Information Storage and Retrieval; D016239:MEDLINE; D008954:Models, Biological; D009154:Mutation; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D012984:Software; D014158:Transcription, Genetic
D005656:Fungal Proteins