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Kboom phenoday-2016

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Presentation on bayesian owl ontology merging approach from Phenoday/Bio-ontologies 2016

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Kboom phenoday-2016

  1. 1. k-BOOM A Bayesian approach to ontology structure inference, with applications in disease ontology construction Chris Mungall Lawrence Berkeley Laboratory PhenoDay 2016 @monarchinit @chrismungall
  2. 2. Building a cohesive, complete disease ontology Objective • Combine existing disease classifications and lists into unified cohesive framework • Best of all worlds • Integrate data from multiple resources Challenges • Current resources developed independently, different perspectives • Mappings are imprecise OMIM Orphanet DO MESH NCIT Deciphe r ICD SNOMED Combined, coherent view
  3. 3. Disease classifications and why mappings are not enough • Given N disease lists – Where each provides cross-references (xrefs) to up to N-1 others – Up to (N^2)-N sets of mappings • Even more with 3rd party mappings – These are frequently • Inconsistent (directly or indirectly) • Different meanings and levels of specificity • Incomplete • Stale • Difficult to computationally verify • Fundamental issue – Xrefs lack semantics – Explicit semantics would enable computational checks Ont1 Ont2 Ont3 Ont4 Ont5 Ont6
  4. 4. DOID (blue) OMIM (brown) MESH (grey) ORDO/Orphanet (yellow) SubClassOf (solid line) Xref (dashed grey line) 4 disease resources plus mappings: Hemolytic anemia
  5. 5. Objective: Coherent OWL Ontology Merging (OOM) • Criteria for OOM – Merged • Combines multiple lists and classifications (terminologies and lists treated as ‘degenerate’ ontologies), Presented as a single ontology • Equivalent classes merged – Logically Connected • OWL/Description Logic constructs – e.g. SubClassOf, EquivalentClass, SomeValuesFrom • Not xrefs – Coherent • Logically coherent: no unsatisfiable classes • Biologically coherent: makes biological and clinical sense
  6. 6. Our previous approach, applied to phenotypes: L-DOOM Logical Definition based OWL Ontology Merging Mungall, C. J., Gkoutos, G., Smith, C., Haendel, M., Lewis, S., & Ashburner, M. (2010). Integrating phenotype ontologies across multiple species. Genome Biology, 11(1), R2. doi:10.1186/gb-2010-11-1-r2 Köhler, S., Doelken, S. C., Ruef, B. J., Bauer, S., Washington, N., Westerfield, M., … Mungall, C. J. (2013). Construction and accessibility of a cross-species phenotype ontology along with gene annotations for biomedical research. F1000Research, 1–12. doi:10.3410/f1000research.2-30.v1 Application to diseases? • Works well for compositional classes (e.g. many cancer terms) • Less well for genetic diseases, complex syndromes 1. Assign Logical Definitions (OWL equivalence axioms) to classes in each ontology • Can be assigned manually or semi- automatically (Obol) HP:0002180 Neuro- degeneration MP:0000876 Purkinje cell degeneration Equiv CL:0000540 neuron CL:0000121 Purkinje cell Equiv degenerate AND inheres-in SOME neuron degenerate AND inheres-in SOME Purkinje cell 2. Using reasoning to infer logical axioms SubClassOf
  7. 7. Probabilistic Ontology OP = <A,H> BOOM Bayes OWL Ontology Merging: Finds the set of hypothetical axioms that maximises P(OP) Merged Coherent OWL Ontology Elk Reasoner Ontology 1 Inter- Ontology Mappings mapping tool Ontology 2 Ontology .. Ontology n Hypothetical Logical Axioms plus Weights (H) mapping curation Axiom Weight Estimator Weight Curation Next iteration Merge equivalent classes
  8. 8. Generating hypothetical logical axioms Inter- Ontology Mappings Hypothetical Logical Axioms plus Weights (H) Axiom Weight Estimator E.g: OMIM:123 xref DOID:987 Pr(OMIM:123 ≡ DOID:987) = 0.3 Pr(OMIM:123 ⊂ DOID:987) = 0.4 Pr(OMIM:123 ⊃DOID:987) = 0.1 Domain rules (lexical, structural, …):
  9. 9. K-BOOM Algorithm for finding most likely merged ontology 1. Factorize calculation by dividing combined axioms into k modules (k-BOOM) Algorithm: i. Assert all hypothetical axioms to be true, ii. Make module from equivalence clique Find values for H that maximises P. Problem: 2^N ontologies hi : boolean representing truth value of hypothetical axiom Hi 2. Use greedy algorithm; start with Most likely hypothetical axioms in Ok 3. Test each configuration using OWL Reasoner (Elk) for satisfiability (unsat => Pr=0), calc posterior probability 4. Repeat until number of tests exceeds threshold 5. Return most likely configuration for Ok
  10. 10. Probability guided curator workflow: A little knowledge goes a long way • Run cycle • Examine results for modules with: – low posterior probability – low confidence (top ranked solution has similar P to next ranked) – Pr(H_i = true) << threshold • Apply biological/clinical knowledge • Override auto-generated hypothetical axiom weights with curated ones – Feedback issues to source ontologies • Repeat dialog Mondo curator External ontology curator
  11. 11. Application: merging diseases into MonDO https://github.com/monarch-initiative/monarch-disease-ontology “Ontology” Classes (before, after merge) SubClass axioms Xrefs Inputs: DOID 6878  6012 7082 36656 MESH (D) 11314  4152 19036 OMIM (D) 7783  7783 0 31242 Orphanet (D) 8740  4683 15182 20326 OMIA 4833  4833 3120 355 DC 209  208 310 316 Medic 0 8630 3435 Output: MonDO 39757  27617 44837 Held back: NCIT, SNOMED, ICD9, GARD
  12. 12. Example Module Resolution: ITM2B amyloidosis
  13. 13. Example failed resolution – due to ontology error https://github.com/monarch-initiative/monarch-disease-ontology/issues/99 https://github.com/DiseaseOntology/HumanDiseaseOntology/issues/164
  14. 14. Example failed resolution – due to mesh duplicates https://github.com/monarch-initiative/monarch-disease-ontology/issues/81
  15. 15. Evaluating results of disease merger • No gold standard for multiple ontology merger – Partial evaluation using held-back Orphanet NTBT/E calls: • 6977/7986 (87% agreement) • Ad-hoc evaluation by curator – Approach: use posterior probabilities to rank modules requiring attention – This is the killer-app feature – Iteratively refine curated probabilities • https://github.com/monarch-initiative/monarch-disease-ontology/issues/ • Results – Manual inspection and use of mondo – Detection of errors in source ontologies • E.g. duplicates in MESH • Incorrect xrefs in DO, e.g. – https://github.com/DiseaseOntology/HumanDiseaseOntology/issues - issues #164, #163, #156, #154, #151, #150, #149, #140, #135
  16. 16. Next Steps • Integrate hypothetical axiom weight estimation into Bayesian model • Apply Markov Chain Monte Carlo (MCMC) methods for estimating most likely graph – E.g Metropolis-Hastings • Integrate other knowledge – Logical Definitions (Phenotypes) – Molecular knowledge • Improve Evaluation – Test k-BOOM on task where we have gold standard, e.g. neuroanatomy/uberon – Formal comparison with EFO, MedGen, …
  17. 17. Discussion • Retrospective merging vs prospective development – Better to work together from outset (OBO model) – However, current state of affairs is such that expert knowledge is distributed across resources – We want to preserve that rather than reinvent – Coherent merging of molecular knowledge with classical top-down knowledge will be required moving forward
  18. 18. Implementation/Availability • Software – https://github.com/monarch-initiative/kboom • Paper – https://github.com/cmungall/kboom-paper – http://biorxiv.org/content/early/2016/04/15/048843 • MonDO – https://github.com/monarch-initiative/monarch- disease-ontology – Both OWL ontology and axiom weight rules
  19. 19. Acknowledgments k-BOOM • Ian Holmes • Sebastian Kohler • Jim Balhoff • Peter Robinson • Melissa Haendel Curation • Nicole Vasilesky (MonDO, DC) • Sue Bello (DC) • Elvira Mitraka (DO) • Lynn Shriml (DO) FUNDING: NIH Office of Director: 1R24OD011883; NIH-UDP: HHSN268201300036C

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