is there life between standards? Data interoperability for AI.
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Presented at the MAQC 2019 conference in Riva del Garda "Reproducibility for Artificial Intelligence in Medicine" https://maqc2019.fbk.eu/
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is there life between standards? Data interoperability for AI.
- 1. Is there life between standards? Increasing data interoperability for AI Prof. @Chris_Evelo Maastricht University
- 3. mine combine use Simple steps in AI? Deposition Databases
- 4. Integrative Systems Biology Internal & external data repositories e.g. dbNP, Sage, Atlas knowledge resources & (semantic web) Integration e.g. Open PHACTS WikiPathways study capturing ISA models study data processing, statistics, storage e.g. arrayanalysis.org ontologies modeling & data integration, network biology (extension), supervised statistics curation, simulation annotation & provenance research applications mapping BridgeDb extraction, SPARQLing conversion
- 5. We can do things like this (diabetic liver) Pihlajamäki et al. dataset is from Gene Expression Omnibus GEO:GSE15653 Pihlajamäki et al. J Clin Endocrinol Metab. 2009, 94 (9): 3521-3529. DOI: 10.1210/jc.2009- 0212. Martina Kutmon et al. BMC Genomics 2014, 15:971. DOI:10.1186/1471-2164- 15-971
- 7. Literature PubChem Genbank Patents Databases Downloads Data Analysis Data Integration Firewalled Databases How do R&D companies use public data?
- 8. How do pharma companies use public data? Pfizer AZ Roche n
- 10. Nanopu b Db VoID Data Cache (Virtuoso Triple Store) Semantic Workflow Engine Linked Data API (RDF/XML, TTL, JSON) Domain Specific Services Identity Resolution Service Chemistry Registration Normalisation & Q/C Identifier Management Service Indexing CorePlatform P12374 EC2.43.4 CS4532 “Adenosine receptor 2a” VoID Db Nanopu b Db VoID Db VoID Nanopu b VoID Public Content Commercial Public Ontologies User Annotations Apps
- 11. Nanopu b Db VoID Data Cache (Virtuoso Triple Store) Semantic Workflow Engine Linked Data API (RDF/XML, TTL, JSON) Domain Specific Services Identity Resolution Service Chemistry Registration Normalisation & Q/C Identifier Management Service Indexing CorePlatform P12374 EC2.43.4 CS4532 “Adenosine receptor 2a” VoID Db Nanopu b Db VoID Db VoID Nanopu b VoID Public Content Commercial Public Ontologies User Annotations Apps
- 14. OxO
- 15. To be done: build an open source chemistry resolution service
- 16. To be done: integrate some traditional bioinformatics services.
- 17. Interoperability for reusability If rocket scientist can reuse rockets we should be able to reuse data.
Editor's Notes
- Standard Interoperability in the Life Sciences
- From: https://xkcd.com/927/ This is basically why we in COST CHARME and in ELIXIR intereoperability platform work on the glue between standards
- Animated slide Showing data and knowledge resources on the left (you can use FAIRsharing to find these). Results are mined from these, combined (where the gluing occurs) and used for AI. This talks focusses on the combine aspects. If you do that correctly the AI part lateron will not have to make the connections and the power can be used to obtain other rseults
- This slide shows the overall approach and the position of the different components/projects
- This study shows we can actually find useful data (in this case liver transcriptomics from human diabetic patients compared to non-diabetic patient data). Process the data, perform pathway enrichment analysis (map to the entities in the pathways) combine pathways into a network (combining overlapping pathways that in the case of WikiPathways need to be mapped again). Extend that network with transcription factors (from e.g. ENCODE, again targets need to be mapped) and look for active nodes in the networks and find the transcription factors that affect these active nodes (essentially turning the network inside out). The result shows the main known transcription factors in diabetics, however using information from just one study not he wealth of information that makes this known.
- A little bit more illustration of how that was done Showing the pathways affected, the overlapping entities and a small representation of the resulting network
- How pharma reused public data
- And every company does the same
- So for the Open PHACTS project we had the idea to link the relevant data together. Using data from ChEMBL (compound-target), NextProt (on the targets), WikiPathways and Reactome (processes and pathways these targets are involved in) and DisGeNET linking the genes coding for these targets to diseases. All that using a. semantic web approach that would make it one big linked dataset
- Alart from the fact that such data is not automatically linked even if you describe it well
- So we added resources that map between textual concepts, ontology terms, database IDs and chemical structures
- Some of such mapping tools are now part of ELIXIR’s recommended interoperability services
- E.g. BridgeDb able to map gene and gene product database IDs and metabolite IDs and more. A BridgeDb based identifier mapping service was part of the original Open PHACTS
- The ontology mapping and crossreference service OxO at EBI, which has not yet been deployed to work on such tasks, but offers the potential to do so
- While the CDK could be used to develop a service that can resolve siubstructures, replacing the original service that was ”not the best open source” project
- And we need classic bioinformatics approaches to map between similar services, domains affected, predicted functions of variants and map SNPs to Indels and such
- And if we get all that done we might be able to reuse data the way rocket scientists reuse their actual rockets (showing the landing of the two side boosters of the very first Falcon Heavy and the first time two of such boosters made it back to earth at the same time, potentially able to be reused)