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OpenDMAP :

OpenDMAP :. An open source, ontology-driven concept analysis engine, with applications to capturing knowledge regarding protein transport, protein interactions and cell-type-specific gene expressions by Hunter, et al. OpenDMAP. Ontology-driven: An ontology is the backbone of the system.

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OpenDMAP :

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  1. OpenDMAP: An open source, ontology-driven concept analysis engine, with applications to capturing knowledge regarding protein transport, protein interactions and cell-type-specific gene expressions by Hunter, et al.

  2. OpenDMAP • Ontology-driven: An ontology is the backbone of the system. • Concept-analysis engine • Each concept in the ontology has an associated recognizer. • A recognizer is a text pattern. • “A pattern describes the words, phrases, parts of speech, syntactic structures or concepts that should cause an instance of the associated concept to be recognized.”

  3. Simple Pattern • Concept: NUCLEUS • Pattern recognizer: NUCLEUS := nucleus, nuclei, nuclear;

  4. Sample ontology

  5. Complex Pattern Regular Expression References to non-terminals (other concepts) Recognizes: “… Bax translocation to mitochondria …”

  6. Another Complex Pattern Fails to recognize: “… Bax translocation to mitochondria from the cytosol …” But the following succeeds: Any order markers

  7. Another Complex Pattern • Fails to recognize: “… H1F-1alpha which is present in glomus cells translocates to the nucleus …” because of the intervening “which” clause. • wild-cards (here, too promiscuous) • syntactic constraints (here, a counter-balance to promiscuity) • The following succeeds:

  8. “Powerful” Patterns Experiments run with just patterns 5–9 augmented with 119 cellular component patterns.

  9. Results* • Run over all abstracts in Medline • Produced 72,460 transport instances • F-measure • 0.29, 10% higher than next best • Recall similar to others, but precision ~20% better (0.39) • Manual check on a small subset • Partial recognition counted • Corrected for protein identification • F-measure raised to 0.81 *Caution: See paper for details.

  10. Discussion Messages • Ontology/Language connection • “[Ensures] that the knowledge representation is grounded [wrt] a carefully constructed model of reality” • “[Recognizers] … structured by the ontology ...” which “… finesses many difficult ambiguity resolution problems” • The architecture makes it easy to exploit better gene/protein recognition systems as they are developed. better any-kind-of recognition systems

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