Powering Biomedical Artificial Intelligence with a Holistic Knowledge Graph (SeWebMeDa2022)
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The document discusses the importance of ontologies in biomedical AI for addressing challenges such as heterogeneous data, complexity, and lack of large datasets. It highlights the need for holistic ontology alignment to support systems biology and personalized medicine through effective mappings and semantic integration. Additionally, it emphasizes the role of knowledge graphs and explainability in improving trust and decision-making in AI applications in biomedicine.
Powering Biomedical Artificial Intelligence with a Holistic Knowledge Graph (SeWebMeDa2022)
- 1. Powering Biomedical Artificial Intelligence with a Holistic Knowledge Graph 5th Workshop on Semantic Web solutions for large-scale biomedical data analytics (SeWeBMeDa) Catia Pesquita clpesquita@ciencias.ulisboa.pt
- 2. Why biomedical AI needs ontologies 1. Large amounts of data in non-standard formats which need to be converted, interpreted, and merged into readable formats. 2. Heterogeneous and complex data which current ML approaches are processing without context 3. Lack of sufficiently large datasets to train DL in some scenarios 4. Lack of explainability Catia Pesquita, LASIGE, ULisboa 2 Ontologies are key to tackle ALL challenges
- 3. What happens when data is complex, heterogeneous and multi-domain? Catia Pesquita, LASIGE, ULisboa 3
- 4. One ontology is not enough Systems Biology, Systems Medicine and Personalized Medicine require holistic representations 4 JD Ferreira, DC Teixeira, C Pesquita. Biomedical Ontologies: Coverage, Access and Use. 2020 Systems Medicine Integrative, Qualitative and Computational Approaches, Academic Press, Elsevier Catia Pesquita, LASIGE, ULisboa
- 5. What we have Ontology Alignment • OA systems find the optimal set of mappings between entities in two ontologies • BioPortal has a mappings repository Logical Definitions • define concepts in terms of other more elementary (atomic) concepts (building blocks) • OBO LDs cover multiple ontologies Catia Pesquita, LASIGE, ULisboa 5
- 6. Available mappings are not enough 55M mappings: • ~2/3 Naïve string matching (label or URI) • ~1/3 UMLS 97% of mappings are skos:closeMatch or skos:relatedMatch 6 Catia Pesquita, LASIGE, ULisboa
- 7. OBO logical definitions are also not enough 7 Schlegel, D. R., Seppälä, S., & Elkin, P. L. (2016). Definition Coverage in the OBO Foundry Ontologies: The Big Picture. In ICBO/BioCreative. Catia Pesquita, LASIGE, ULisboa
- 8. State-of-the-art OM systems are not enough 8 Simple equivalences Anatomy: 94.1% F-measure Large Bio: 72-84% F-measure Biodiversity: 81-84% F-measure Catia Pesquita, LASIGE, ULisboa
- 9. What do we need to create holistic representations? 9 Cover multiple domains Align multiple ontologies Scalability Ensure rich semantic integration Related but not equal domains Complex relations involving more than one ontology Provide high quality alignments Support human interaction Visualize the context of a mapping Balance cognitive overload and informativeness MC Silva, D Faria, and C Pesquita. Integrating knowledge graphs for explainable artificial intelligence in biomedicine. Ontology Matching workshop 2021 Catia Pesquita, LASIGE, ULisboa
- 10. Rethinking biomedical ontology alignment 10 Cover multiple domains Pairwise ontology alignment Holistic ontology alignment of multiple ontologies Ensure rich semantic integration Simple equivalence ontology alignment Complex ontology alignment Provide high quality alignments User validation Human-in-the-loop Interactive Alignment MC Silva, D Faria, and C Pesquita. Integrating knowledge graphs for explainable artificial intelligence in biomedicine. Ontology Matching workshop 2021 Catia Pesquita, LASIGE, ULisboa
- 11. Holistic Matching (CIA) runs in half the time, aims for high recall within similar domains and high precision across domains 11 Silva, M.C., Faria, D. & Pesquita, C. (2022). Matching Multiple Ontologies to Build a Knowledge Graph for Personalized Medicine. ESWC2022 (See it on Wednesday 11:30) Catia Pesquita, LASIGE, ULisboa
- 12. Beyond simple equivalence matching 12 Two ontologies Similar domain Different granularity Subsumption Matching Two ontologies Similar domain Different model Complex Matching Respiratory tract infection Respiratory finding Respiratory tract infection Respiratory tract Infection abnormal immune system cell morphology abnormal immune system cell morphology Multiple ontologies Different domains Compound Matching Catia Pesquita, LASIGE, ULisboa
- 13. Complex Matching with Targeted Pattern Mining Can we improve the performance of COM by using targeted pattern mining-based algorithms? Requires shared (or matched) instances Patterns are used a priori, to tailor Association Rule Mining More effective, as we optimise the search and selection of each pattern More efficient, as we reduce the search space 13 Lima, B., Faria, D. & Pesquita, C. (2021). Pattern-Guided Association Rule Mining for Complex Ontology Alignment. ISWC2021 P &D. Catia Pesquita, LASIGE, ULisboa
- 14. Complex Matching with targeted pattern mining finds good precision mappings – many new Manual evaluation of the OAEI cmt-conference alignment 14 Lima, B., Faria, D. & Pesquita, C. (2021). Pattern-Guided Association Rule Mining for Complex Ontology Alignment. ISWC2021 P &D. Catia Pesquita, LASIGE, ULisboa
- 15. Compound Matching for ontology triples Can we find mappings involving multiple ontologies using lexical approaches and search space pruning? 15 HP:0001650 aortic stenosis PATO:0001847 constricted Step 1 FMA:3734 aorta Step 2 HP:0001650 (…) stenosis Filter unmapped source classes Remove mapped words from class labels. Selection of best scoring mappings Oliveira, D., & Pesquita, C. (2018). Improving the interoperability of biomedical ontologies with compound alignments. J. Biomedical Semantics. Catia Pesquita, LASIGE, ULisboa
- 16. Complex Matching finds (mostly) high precision ternary mappings – many new 16 Ontology sets Mappings Correct (New) Incorrect MP-CL-PATO 448 47.1% (17.6%) 17.6% MP-GO-PATO 875 86.9% (22.3%) 4.1% MP-NBO-PATO 169 70.4% (20.7%) 0.0% MP-UBERON-PATO 1413 83.5% (24.7%) 2.9% WBP-GO-PATO 272 44.9% (33.3%) 4.7% HP-FMA-PATO 1270 81.5% (44.1%) 4.1% Example of correct new mapping for MP-UBERON-PATO “absent thoracic vertebrae” (MP:0004655) → “thoracic vertebra” (UBERON:0002347) and “lacks all parts of type” (PATO:0002000). Mappings were evaluated against existing logical definitions and a subset of new mappings was evaluated by two specialists Oliveira, D., & Pesquita, C. (2018). Improving the interoperability of biomedical ontologies with compound alignments. J. Biomedical Semantics. Catia Pesquita, LASIGE, ULisboa
- 17. https://github.com/liseda-lab/VOWLMap Supporting contextualized ontology alignment validation 17 Guerreiro, A., Faria, D. & Pesquita, C. (2021) VOWLMap: Graph-based Ontology Alignment Visualization and Editing. VOILA workshop (ISWC 2021) Catia Pesquita, LASIGE, ULisboa
- 18. Graph visualization and interaction supports context and is both preferred and more frequently used • Running user evaluations with domain experts is a challenge • Still not true human-in-the-loop 18 Guerreiro, A., Faria, D. & Pesquita, C. (2021) VOWLMap: Graph-based Ontology Alignment Visualization and Editing. VOILA workshop (ISWC 2021) Catia Pesquita, LASIGE, ULisboa
- 19. Learning with multiple ontologies Catia Pesquita, LASIGE, ULisboa 19
- 20. Knowledge Graph Embeddings for ICU readmission prediction 20 Catia Pesquita, LASIGE, ULisboa
- 21. 21 Catia Pesquita, LASIGE, ULisboa
- 22. Knowledge Graph Embeddings for ICU readmission prediction 22 • Prevents 40% of too early ICU releases while ensuring all patients that are indeed released do not return to the ICU Catia Pesquita, LASIGE, ULisboa Carvalho, R.., D. Oliveira., C. Pesquita "Knowledge Graph Embeddings for ICU readmission prediction." (2022).https://doi.org/10.21203/rs.3.rs-1507573/v1
- 23. Predicting Gene-Disease Associations Can using more than one ontology and exploring logical definitions improve the prediction of gene- disease associations based on KG embeddings? 23 Nunes, S., Sousa, R. T., & Pesquita, C. (2021). Predicting Gene-Disease Associations with Knowledge Graph Embeddings over Multiple Ontologies. Bio-Ontologies COSI-ISMB/ECCB. Catia Pesquita, LASIGE, ULisboa
- 24. Predicting Gene-Disease Associations 24 Nunes, S., Sousa, R. T., & Pesquita, C. (2021). Predicting Gene-Disease Associations with Knowledge Graph Embeddings over Multiple Ontologies. Bio-Ontologies COSI-ISMB/ECCB. Weighted Average F-measure Catia Pesquita, LASIGE, ULisboa
- 25. What about explainability and trust? Catia Pesquita, LASIGE, ULisboa 25
- 26. Trust in AI • the user successfully comprehends how the model arrives at an outcome 🡪 represent inputs, outputs and processes • the model’s outcomes/workings match the user’s prior knowledge 🡪 build a shared context 26 Jacovi, Alon, et al. "Formalizing trust in artificial intelligence: Prerequisites, causes and goals of human trust in AI." Proceedings of the 2021 ACM conference on fairness, accountability, and transparency. 2021. Catia Pesquita, LASIGE, ULisboa
- 27. Trust in AI • the user successfully comprehends how the model arrives at an outcome → represent a model’s processes represent inputs, outputs and • the model’s outcomes/workings match the user’s prior knowledge → evaluation 27 Catia Pesquita, LASIGE, ULisboa
- 28. Trust in AI for biomedical and clinical applications • the user successfully comprehends how the model arrives at an outcome → represent a model’s processes represent inputs, outputs and • the model’s outcomes/workings match the user’s prior knowledge → evaluation 28 Catia Pesquita, LASIGE, ULisboa
- 29. Trust in AI for biomedical and clinical applications • the user successfully comprehends how the model arrives at an outcome → represent a model’s inputs, outputs and processes • the model’s outcomes/workings match the user’s prior knowledge → evaluation 29 Catia Pesquita, LASIGE, ULisboa
- 30. Trust in AI for biomedical and clinical applications • the user successfully comprehends how the model arrives at an outcome → represent a model’s inputs, outputs and processes • the model’s outcomes/workings match the user’s prior knowledge → represent the scientific context 30 Catia Pesquita, LASIGE, ULisboa
- 31. Knowledge Science can help mitigate bias in biomedical AI 31 10.1038/s43856-021-00028-w Mitigating bias in machine learning for medicine Catia Pesquita, LASIGE, ULisboa
- 32. Knowledge Science for Trust in Biomedical AI Assessing trustworthiness requires data, domain and user context Data context: represent data provenance and transformations/processing Domain/background knowledge: represent the scientific context of the data and application User context: different users will trust based on different expectations 32 Catia Pesquita, LASIGE, ULisboa
- 33. Data Context Augment explanations with the data creation and processing context • Provide a rich contextual semantic layer to the underlying data using domain ontologies and knowledge graphs. • Preserve uncertainty and highlight potential ambiguity and incompleteness at the data level 33 Catia Pesquita, LASIGE, ULisboa
- 34. Data Context is key for trust 25% of the works that developed ML approaches to diagnose COVID-19 in adults based on chest X-rays and CT scans used pediatric (ages 1-5) pneumonia images as control. 34 Roberts, Michael, et al. "Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans." Nature Machine Intelligence 3.3 (2021): 199-217. 10.1016/j.cell.2018.02.010 COVID-19 10.1016/j.rxeng.2020.11.001 Catia Pesquita, LASIGE, ULisboa
- 35. Domain Knowledge Context Contextualize an explanation within existing knowledge • Include prior knowledge through links to ontologies • Enrich the contextual semantic layer with links and relations across domains of knowledge 35 Catia Pesquita, LASIGE, ULisboa
- 36. Domain Knowledge Context is key for trust 36 Term 1 Term 2 Similarity Gingiva Gum 0.98 Catia Pesquita, LASIGE, ULisboa
- 37. Domain Knowledge Context is key for trust 37 Gingiva Gum 0.98 Term 1 Term 2 Similarity Gingiva Gum 0.98 Anatomical Part Chemical Substance Catia Pesquita, LASIGE, ULisboa
- 38. User Context Trusting an AI outcome depends on the user context: task, prior knowledge, expectation, etc. 38 Protein A and Protein B are not similar at all since they are involved in unrelated diseases… Protein A and Protein B are very similar because they perform the same molecular function! Biochemist Clinician Catia Pesquita, LASIGE, ULisboa
- 39. Ontologies can support XAI Reasoning • Patient has Cough and Cough is_a Respiratory Finding → Patient has Respiratory Finding Querying • find all patients annotated with Fever and Respiratory Finding Similarity modelling • find all patients with similar profiles (semantic similarity) 39 XAI Catia Pesquita, LASIGE, ULisboa
- 40. Explaining Protein-Protein Interaction (PPI) Predictions 40 Protein P1 Protein P2 ? Experts want to understand the biological mechanisms that underlie the natural phenomena they are predicting. Catia Pesquita, LASIGE, ULisboa
- 41. Catia Pesquita, LASIGE, ULisboa 41 Combining Genetic Programming and Semantic Similarity # entity pairs Computing KG-based semantic similarity between entity pairs for each semantic aspect Evolving a GP model Predicting on unseen data using the GP model # semantic aspects targets GO:0005575 cellular component GO:0003674 molecular function GO:0008151 biological process GO:0110165 cellular anatomical entity GO:0005615 extracellular space GO:0062167 complement component C1q complex GO:0008152 metabolic process GO:0070085 glycosylation GO:0036065 fucosylation GO:0005488 binding GO:0097159 organic cyclic compound binding GO:0003676 nucleic acid binding P1 P2 Gene Ontology KG P3 PPI from STRING Database Random Negative Sampling P1 P2 1 P1 P3 1 P2 P3 0 Median weighted average of F-measures (WAF) RandomForest 0.914 GP6x 0.866
- 42. 42 40S ribosomal protein S12 and 40S ribosomal protein S10 A Model That Fits with Biology GO:0008151 biological process GO:0051179 localization GO:0000184 nuclear-transcribed mRNA catabolic process, nonsense-mediated decay S12 S10 GO:0006614 SRP-dependent cotranslational protein targeting to membrane GO:0009987 cellular process K1 T𝜷6 GO:0005874 microtubule GO:0110165 cellular anatomical entity GO:0005575 cellular component GO:0008151 biological process GO:0009987 cellular process GO:0009987 cellular component organization True Positive (+/+) True Negative (-/-) cellular anatomical entity binding structural molecule activity interspecies interaction between organisms metabolic process biological regulation protein containing complex localization cellular process 0.0 0.2 0.4 0.6 0.8 1.0 Semantic Similarity 0.0 0.2 0.4 0.6 0.8 1.0 Semantic Similarity cellular anatomical entity binding cellular process Kinetochore-associated protein 1 and Tubulin B-6 chain max(SS catalytic activity, SS cellular process, SS molecular adaptor activity, SS molecular function regulator, SS multicellular organismal process, SS signaling, SS behavior + SS immune system process) Catia Pesquita, LASIGE, ULisboa
- 43. But is even more interesting when it fails 43 Protein S100-A10 works together with neuroblast differentiation-associated protein AHNAK in the development of the intracellular membrane, but this information is missing from the GO annotations. S100 -A10 AHNAK GO:0045121 membrane raft GO:0110165 cellular anatomical entity GO:0005575 cellular component GO:0070062 extracellular exosome S100-A10 protein and neuroblast differentiation-associated protein AHNAK False Negative (+/-) 0.0 0.2 0.4 0.6 0.8 1.0 Semantic Similarity cellular anatomical entity binding biological regulation Catia Pesquita, LASIGE, ULisboa
- 44. But is even more interesting when it fails 44 The literature describes interactions between proteins of the same family of the pair, indicating that this is likely a true but still unknown interaction. GO:0008151 biological process GO:0000165 MAPK cascade Dlg2 GO:0016323 basolateral plasma membrane GO:0005575 cellular component GO:0110165 cellular anatomical entity GO:0051179 localization GO:0009987 cellular process GO:0065007 biological regulation Protransforming growth factor 𝜶 and Disks large homolog 2 False Positive (-/+) 0.0 0.2 0.4 0.6 0.8 1.0 Semantic Similarity cellular anatomical entity binding localization metabolic process biological regulation cellular process Catia Pesquita, LASIGE, ULisboa
- 45. Explainable AI for Personalized Oncology 45 katy-project.eu Catia Pesquita, LASIGE, ULisboa KATY is built around two main components: A Distributed Knowledge Graph and a pool of eXplainable Artificial Intelligence predictors.
- 46. Building a KG for Explainable AI for Personalized Oncology 46 Catia Pesquita, LASIGE, ULisboa
- 47. Patient Ex-smoker Smoker Diabetes MET HLA-A2 cytokine-mediated signaling pathway Renal cell carcinoma, somatic Sunitinib Antineoplastic Agent Tyrosine kinase activity Cancer AI recommendation presents has mutation inhibits The KG can be used to contextualize the patient and the drug recommendation 47 promotes treats Patient treated with is risk factor Catia Pesquita, LASIGE, ULisboa
- 48. Patient Ex-smoker Smoker Diabetes MET HLA-A2 cytokine-mediated signaling pathway Renal cell carcinoma, somatic Sunitinib Antineoplastic Agent Tyrosine kinase activity Cancer AI recommendation presents has mutation inhibits The KG can be used to contextualize the patient and the drug recommendation 48 promotes treats Patient treated with is risk factor How diverse is my dataset in terms of patient features? Catia Pesquita, LASIGE, ULisboa
- 49. Patient Ex-smoker Smoker Diabetes MET HLA-A2 cytokine-mediated signaling pathway Renal cell carcinoma, somatic Sunitinib Antineoplastic Agent Tyrosine kinase activity Cancer AI recommendation presents has mutation inhibits The KG can be used to contextualize the patient and the drug recommendation 49 promotes treats Patient treated with is risk factor How **similar** are my negative and positive cases? Catia Pesquita, LASIGE, ULisboa
- 50. Patient Ex-smoker Smoker Diabetes MET HLA-A2 cytokine-mediated signaling pathway Renal cell carcinoma, somatic Sunitinib Antineoplastic Agent Tyrosine kinase activity Cancer AI recommendation presents has mutation inhibits The KG can be used to contextualize the patient and the drug recommendation 50 promotes treats Patient treated with is risk factor How does the prediction match current scientific knowledge? Catia Pesquita, LASIGE, ULisboa
- 51. Patient Ex-smoker Smoker Diabetes MET HLA-A2 cytokine-mediated signaling pathway Renal cell carcinoma, somatic Sunitinib Antineoplastic Agent Tyrosine kinase activity Cancer AI recommendation presents has mutation inhibits The KG can be used to contextualize the patient and the drug recommendation 51 promotes treats Patient treated with is risk factor How **similar** are patients treated with the same drug? Catia Pesquita, LASIGE, ULisboa
- 52. What’s next? 52 Holistic Matching Interactive Matching Complex Matching KG-based Biomedical AI Explainable Biomedical AI Catia Pesquita, LASIGE, ULisboa
- 53. Trustworthy biomedical AI requires trustworthy data. Trustworthy data requires semantics. Catia Pesquita, LASIGE, ULisboa
- 54. Acknowledgements Daniel Faria, LASIGE/Biodata.pt, Portugal Sara Silva, LASIGE, Portugal Isabel Cruz, U. Illinois, USA Daniela Oliveira, Novartis Booma S. Balasubramani, Microsoft and many others Past and present students: Rita Sousa Marta Silva Susana Nunes Ricardo Carvalho Ana Guerreiro Patrícia Eugénio Filipa Serrano Beatriz Lima Carlota Cardoso and many others Catia Pesquita, LASIGE, ULisboa 54 This work was supported by FCT through the LASIGEResearch Unit (UIDB/00408/2020 and UIDP/00408/2020). It was also partially supported by the KATY project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101017453.
- 55. Catia Pesquita clpesquita@fc.ul.pt @CPesquita www.di.fc.ul.pt/~catiapesquita liseda-lab.github.io www.lasige.pt 55 The Lisbon Semantic Data Lab (LiSeDa) at LASIGE is a multidisciplinary research group that works at the intersection between semantic technologies and data science with a strong focus in biomedical and life sciences applications. Contact us to learn more about open positions and collaboration opportunities. Catia Pesquita, LASIGE, ULisboa