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Making sense of ICA data : ML and knowledge graphs

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semantic data integration and machine learning with intracranial aneurysms datasets

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Making sense of ICA data : ML and knowledge graphs

  1. 1. Making sense 
 of ICA data: how to ? semantic data integration, machine learning Alban Gaignard, PhD, CNRS Nantes, 06 december 2018 « La folle journée de l’anévrismes »
  2. 2. Multiscale observations 2 ICAN cohort, intracranial aneursyms, 30 centers predict risk of rupture
 better understand mecanisms, causes
 hopefully prevent
  3. 3. Multiscale observations 2 ! ! ! ICAN cohort, intracranial aneursyms, 30 centers predict risk of rupture
 better understand mecanisms, causes
 hopefully prevent
  4. 4. Multiscale observations 2 ! ! ! ICAN cohort, intracranial aneursyms, 30 centers predict risk of rupture
 better understand mecanisms, causes
 hopefully prevent
  5. 5. Multiscale observations Challenges • data silos • interoperability • data diversiy • ≠ vocabularies / semantics • variables >> samples (N) • decentralization 2 ! ! ! ICAN cohort, intracranial aneursyms, 30 centers predict risk of rupture
 better understand mecanisms, causes
 hopefully prevent
  6. 6. « AI » : many expectations
  7. 7. Vocabulary Artificial Intelligence (AI) == solving problems with machines « intelligently ».  
 Some AI tasks include • Knowledge representation and reasoning (experts systems) • Pattern recognition (computer vision, speech recognition, natural language processing) : need training data + annotations
 Machine Learing (ML) == a technique to automatically detect or predict based on learned examples. No programming is required. 
 4
  8. 8. Some ML algorithms Lot of tuning/testing required …
  9. 9. Explain or predict ? 6 Explainability Prediction 
 performance Decision trees Random Forests Artificial Neural Networks
  10. 10. Trusting black boxes ? 7 Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16). ACM, New York, NY, USA, 1135-1144. DOI: https://doi.org/10.1145/2939672.2939778
  11. 11. Trusting black boxes ? 7 Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16). ACM, New York, NY, USA, 1135-1144. DOI: https://doi.org/10.1145/2939672.2939778
  12. 12. Trusting black boxes ? 7 Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16). ACM, New York, NY, USA, 1135-1144. DOI: https://doi.org/10.1145/2939672.2939778
  13. 13. Trusting black boxes ? 7 Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16). ACM, New York, NY, USA, 1135-1144. DOI: https://doi.org/10.1145/2939672.2939778 Good predictions, but for « wrong » reasons. (biased training data)
  14. 14. Knowledge graphs Computational ontology : « a formal specification of a shared conceptualization » (Borst, 1997) 8
  15. 15. Knowledge graphs Computational ontology : « a formal specification of a shared conceptualization » (Borst, 1997) 8 Human Phenotype Ontology : Cerebral berry aneurysm Dilatation of the cerebral artery Abnormal cerebral artery morphology Abnormal systemic arterial morphology Abnormal vascular morphology Abnormal vascular morphology Abnormality of the cardiovascular system is a
  16. 16. ➞ Contextual information for better interpretation, reuse, cross-linking of imaging measures.
  17. 17. ➞ Contextual information for better interpretation, reuse, cross-linking of imaging measures.
  18. 18. d1, d2 : distances between bifurcations r1, r2, r3 : radius/diameters of bifurcations   𝜃1, 𝜃2 : angles of bifurcations
  19. 19. d1, d2 : distances between bifurcations r1, r2, r3 : radius/diameters of bifurcations   𝜃1, 𝜃2 : angles of bifurcations r1 Diameter Morph. parameter Anat. measurement is a is a is a has unit « mm » Dilatation of the cerebral artery is a is measurement of MR device is generated by Segmentation algo. has value « 2.3 » Cerebral berry aneurysm is estimated by
  20. 20. has-genotype has-image consumes-tobaccoconsumes-alcohol has-father has-mother is-present-for-individual position chromosme ref-allele alt-allele loss-of-function gene-id GWAS SNP 1/1 has-variant has-value
  21. 21. has-genotype has-image consumes-tobaccoconsumes-alcohol has-father has-mother is-present-for-individual position chromosme ref-allele alt-allele loss-of-function gene-id GWAS SNP 1/1 has-variant has-value
  22. 22. has-genotype has-image consumes-tobaccoconsumes-alcohol has-father has-mother is-present-for-individual position chromosme ref-allele alt-allele loss-of-function gene-id GWAS SNP 1/1 has-variant has-value
  23. 23. has-genotype has-image consumes-tobaccoconsumes-alcohol has-father has-mother is-present-for-individual position chromosme ref-allele alt-allele loss-of-function gene-id GWAS SNP 1/1 has-variant has-value
  24. 24. has-genotype has-image consumes-tobaccoconsumes-alcohol has-father has-mother is-present-for-individual position chromosme ref-allele alt-allele loss-of-function gene-id GWAS SNP 1/1 has-variant has-value
  25. 25. Work in progress …
  26. 26. 13 features : hypertension, age, localisation, size, antecedents
  27. 27. Random Forests experiments Objective : predict ICA rupture based on the available clinical observations (BMI, tobacco, alcool, ICA localization, etc. ) 14
  28. 28. Random Forests variable ranking 15
  29. 29. Random Forests experiments 16 overfitting ? 10-folds cross validation
 Accuracy: 0.59 (+/- 0.04)
  30. 30. INEX-MED national pilot project 17 Congenital myopathies Intracranial aneurysms Challenges Molecular diagnosis fails in more than 
 50% of cases : 
 - more than 30 known genes - several non-specific phenotypes Complex and multi-scale biological 
 phenomenons involved : - Life habits, image-based evaluation ? - Genetic variant and image phenotypes ? - Mirror aneurysms patient sub-grouping ? Objective Combine exomes, histopathological 
 imaging and clinical observations to 
 better rank genetic variants. Combine genomic data, MR imaging and clinical observationso better unerstand and predict ICA evolution schemes. Partners Strasbourg : IGBMC, iCube Paris : Pitié Salpétrière (Institut de Myologie) Nantes : institut du thorax, LS2N, CHU de Nantes Rennes : INRIA-IRISA
  31. 31. INEX-MED roadmap (2018-2020) Intracranial aneurysms Congenital myopathies
  32. 32. Representing and linking cerebral vascular imaging biomarkersclinics genetics subject ?x
  33. 33. Massive on-the-fly image processing … Neuroimaging data, 
 Shanoir, Rennes periodically « useful » image polling knowledge graphImage feature extraction, 
 BiRD, Nantes (~2h per image) Semantic alignement
 + linking Academic hospitals
  34. 34. Recap Many branches in artificial intelligence Machine learning approaches can be « black boxes »
 Knowledge graphs can facilitate • definition of common vocabularies 
 community effort ! experts needed ! • precise selection of data with their context (metadata) • interoperable data exchanges, sharing, reuse 
 Still lot of work to be done : INEX-MED project, others ? 21
  35. 35. Matilde Karackachoff, Hala Skaf-Molli, Richard Redon, Christian Dina, Vincent L’Allinec, Romain Bourcier, Hubert Desal, Florent Autrusseau, Anass Nouri, Maxime Folschette Julie Thompson, Norma Romero, Olivier Poch, Kirsley Chennen, Jocelyn Laporte, BiRD facility, NeurInfo facility ICAN consortium Acknowledgments

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