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Bio-Medical Interaction Extractor

Bio-Medical Interaction Extractor. Syed Toufeeq Ahmed ASU. Scopes. Various syntactic roles (such as Subject , Object and Modifying phrase) and their linguistically significant combinations makes up SCOPES A SCOPE MATCHING is:

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Bio-Medical Interaction Extractor

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  1. Bio-Medical Interaction Extractor Syed Toufeeq Ahmed ASU

  2. Scopes • Various syntactic roles (such as Subject , Object and Modifying phrase) and their linguistically significant combinations makes up SCOPES • A SCOPE MATCHING is: • Elementary (E) : If the scope contains a Gene /Protein (G) name or an interaction word (I). • Partial (P) : If the scope has a Gene/Protein (G) name and an interaction word (I). • Complete (C) : If the scope has at least two Gene /Protein (G) names andan interaction word (I).

  3. Scopes “HMBA could inhibit the MEC-1 cell proliferation by down-regulation of PCNA expression.” Elementary (Subject) Elementary (Object) Interaction (Verb) Partial (Modifying Phrase)

  4. Scopes & Matches “The kinase phosphorylation of Gene1 by Gene2 could inhibit Gene3. ” Complete (Subject)

  5. Algorithm of Interaction Extractor: Is Main Verb an Interaction (I) ? Interaction : { G1, I, G2 } Interaction : { G1, I, G2 } S-O S-M S O MP Subject Object Modifying Phrase Elementary (G1) Partial (I,G2) Elementary (G2) complete (G,I,G)  interact: {G,I,G} complete (G,I,G)  interact: {G,I,G} complete (G,I,G)  interact: {G,I,G}

  6. Example “HMBA could inhibit the MEC-1 cell proliferation by down-regulation of PCNA expression.” Main Verb (I) { “HMBA”, “down-regulation”, “PCNA expression”} Elementary (G) Elementary (G) { “HMBA”, “inhibit”, “the MEC-1 cell proliferation” } Partial

  7. Next Steps • Handling negations in the sentences (such as “not interact”, “fails to induce”, “does not inhibit”). • Extraction of detailed contextual attributes of interactions (such as bio-chemical context or location) by interpreting modifiers: • Location/Position modifiers (in, at, on, into, up, over…) • Agent/Accompaniment modifiers (by, with…) • Purpose modifiers( for…) • Theme/association modifiers ( of..) • Extraction of relationships between interactions from among multiple sentences in abstracts (signaling pathways)

  8. Next Steps • Visualization of Signaling Pathways

  9. Preliminary Results

  10. References • Link Grammar: http://www.link.cs.cmu.edu/link • LocusLink: http://www.ncbi.nlm.nih.gov/LocusLink • UMLS: http://www.nlm.nih.gov/research/umls/umlsmain.html

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