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Bio-Medical Interaction Extractor

This study delves into the extraction of bio-medical interactions by analyzing various syntactic roles, such as subjects, objects, and modifying phrases. The concept of SCOPES is introduced, categorizing interactions into elementary, partial, and complete based on the presence of gene/protein names and interaction words. The algorithm developed identifies main verbs of interactions while incorporating advanced next steps, including handling negations and contextual attributes. Preliminary results and future visualization of signaling pathways are discussed, with references to essential bioinformatics resources.

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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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