Aggregating automatically extracted regulatory pathway relations
Automatic tools to extract information from biomedical texts are needed to help researchers leverage the vast and increasing body of biomedical literature. While several biomedical relation extraction systems have been created and tested, little work has been done to meaningfully organize the extrac...
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| Veröffentlicht in: | IEEE journal of biomedical and health informatics 2006-01, Vol.10 (1), p.100-108 |
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| container_title | IEEE journal of biomedical and health informatics |
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| creator | Marshall, B. Hua Su McDonald, D. Eggers, S. Chen, H. |
| description | Automatic tools to extract information from biomedical texts are needed to help researchers leverage the vast and increasing body of biomedical literature. While several biomedical relation extraction systems have been created and tested, little work has been done to meaningfully organize the extracted relations. Organizational processes should consolidate multiple references to the same objects over various levels of granularity, connect those references to other resources, and capture contextual information. We propose a feature decomposition approach to relation aggregation to support a five-level aggregation framework. Our BioAggregate tagger uses this approach to identify key features in extracted relation name strings. We show encouraging feature assignment accuracy and report substantial consolidation in a network of extracted relations |
| doi_str_mv | 10.1109/TITB.2005.856857 |
| format | Article |
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(IEEE) 2006</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c438t-9514d33f84f2f965057784269b92f666fd98c3629ef36b5fbecabec5ea60d26b3</citedby><cites>FETCH-LOGICAL-c438t-9514d33f84f2f965057784269b92f666fd98c3629ef36b5fbecabec5ea60d26b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1573712$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,778,782,794,27911,27912,54745</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1573712$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16445255$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Marshall, B.</creatorcontrib><creatorcontrib>Hua Su</creatorcontrib><creatorcontrib>McDonald, D.</creatorcontrib><creatorcontrib>Eggers, S.</creatorcontrib><creatorcontrib>Chen, H.</creatorcontrib><title>Aggregating automatically extracted regulatory pathway relations</title><title>IEEE journal of biomedical and health informatics</title><addtitle>TITB</addtitle><addtitle>IEEE Trans Inf Technol Biomed</addtitle><description>Automatic tools to extract information from biomedical texts are needed to help researchers leverage the vast and increasing body of biomedical literature. 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| subjects | Abstracts Algorithm design and analysis Artificial Intelligence Data mining Feature extraction Information analysis Information Storage and Retrieval - methods Knowledge representation Models, Biological Natural Language Processing Object recognition Organizing Pattern Recognition, Automated Proteome - metabolism regulatory pathway analysis relation parsing Signal Transduction - physiology Spatial databases System testing Visualization Vocabulary, Controlled |
| title | Aggregating automatically extracted regulatory pathway relations |
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