From Queries to Algorithms to Advanced ML: 3 Pharmaceutical Graph Use Cases
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The document discusses the application of graph databases in pharmaceuticals, specifically focusing on connecting disparate data silos to generate new insights in biomedical research. It highlights various use cases, such as identifying genes associated with diseases, employing natural language processing, and utilizing graph algorithms for sub-phenotyping diabetic patients. Additionally, it emphasizes the scalability and flexibility of graph databases compared to traditional relational models, underlining their potential for knowledge integration and advanced analysis.
![Query „friends of a friend“ on a gene level
Example: diabetes relevant gene ‚TCF7L2’
match path=(g:Gene{sid:'TCF7L2'})-[:MAPS|SYNONYM*0..2]-(g1:Gene) return path](https://image.slidesharecdn.com/alexanderjaraschbiodatadzdconnectknowledgegraph-211116082350/85/From-Queries-to-Algorithms-to-Advanced-ML-3-Pharmaceutical-Graph-Use-Cases-20-320.jpg)
![Query for SNPs (mutations) associated to diabetes
Output: relevant protein and its function (ontology terms)
match (tr:Trait)
where tr.name contains ‚diabetes mellitus‘
with tr as disease
match path=(disease)<-[:ASSOCIATED_WITH_TRAIT]-(asso:Association)<-[:SNP_HAS_ASSOCIATION]-(snp:SNP)-
[:SNP_HAS_GENE]-(gene:Gene)-[:MAPS]-(g1:Gene)-[x:CODES]->(transcript:Transcript)-[:CODES]->
(prot:Protein)-[:ASSOCIATION]->(term:Term)—(o:Ontology)
return path](https://image.slidesharecdn.com/alexanderjaraschbiodatadzdconnectknowledgegraph-211116082350/85/From-Queries-to-Algorithms-to-Advanced-ML-3-Pharmaceutical-Graph-Use-Cases-22-320.jpg)
From Queries to Algorithms to Advanced ML: 3 Pharmaceutical Graph Use Cases
- 1. From Advanced Queries to Algorithms to Advanced ML: 3 Pharmaceutical Graph Use Cases Dr. Alexander Jarasch
- 2. • 5 partners + assoc. partners • 450 researchers • bundles basic research and clinical trials expertise • => variety of data => unstructured => heterogeneous => not connected => unFAIR
- 3. DZD Data and Knowledge Management team Dr. Alexander Jarasch Justus Täger Tim Bleimehl Angela Dedie Yaroslav Zdravomyslov
- 4. The Challenge Connecting data (silos) -> get new insights Easy question -> Difficult to answer
- 5. The Challenge Variety of users / diversity of scientific questions Scientists Medical Doctors Data Scientists Graphdatabase
- 6. Biological question: Are human T2D genes enzymes acting on metabolites which in turn are regulated in pig diabetes model? The actual question (from a data-point-of-view): Is there a connection between A and R? => 3s to look into the Excel sheet Why graph? Easy scientific question
- 7. The actual question (from a data-point-of-view): Is there a connection between A and R? => 3s to look into the graph A B C E D F G K Q R S W Z U Why graph? Easy scientific question
- 8. Back to the question Are human T2D genes enzymes acting on metabolites which in turn are regulated in pig diabetes model? Genomics Human diabetic data Genes SNPs Proteins Enzymes Pathways Metabolites Metabolomics Pre diabetic pig Metabolites List of SNPs List of Genes of (species 1) List of Proteins of (species 1) List of loci List of Enzymes of (species 1) List of Pathways of (species 1) List of Metabolites of (species 1) List of Metabolites of (species 2) graph
- 9. Why graph? -> why not relational • biomedical data / healthcare data is highly connected • => variety of data => unstructured => heterogeneous => not connected => unFAIR • easy to model • extremely flexible / easy adoptable („re-shaping the graph“) vs. static SQL model • scalable (Billion of nodes+relationships on a single machine • easy to query (cyclic dependencies) • GraphDataScience library + graph embeddings
- 10. Alzheimer‘s cancer cardio vascular diseases diabetes Lung diseases infectious diseases new hypotheses Diseases are connected
- 11. DZDconnect: Concept DZD in-house data Natural Language Processing Inferring knowledge Knowledge Graph
- 12. DZDconnect: stats • PROD-Server: 323m nodes, 1.1bn relationships => 480GB • DEV-Server: 1.1bn nodes, 4.8bn relationships • Singleserver (60 CPUs, 256GB memory, only SSDs) • 4 developers • Neo4j enterprise (live backup, GDS) • UI: flask web server, SemSpect, Neo4j browser • Visualization for interactive browsing (SemSpect by derive GmbH) • Bloom (semi-natural-language queries) Strata Data Award finalist 2019 bytes4diabetes Award 2020 Graphie Award 2018 We have DB role model
- 13. DZDconnect: data integration + ML Gene RNA Protein CODES CODES CODES* • Python • Py2Neo, GraphIO • Docker Pipeline for orchestration (open-source by DZD) • Based on integrated data => annotate / enrich • textmatching + Natural Language Processing • „shortcuts“ for queries (reduce #hops) • inferring knowledge
- 14. DZDconnect: data model <-> human readable = easy to query
- 16. The Challenge User with a specific input => specific output Scientist multi-omics experiment output Flask app
- 17. The Challenge User ”start somewhere -> explore freely knowledge” SemSpect interactive browsing Start from any node Scientist or Medical Doctor
- 18. The Challenge User with data analysis skills / computer scientist Scientist Start from any node Cypher query language Graph Data Science
- 19. Use case 1 Handle mapping identifiers of molecular entities Knowledge Graph
- 20. Query „friends of a friend“ on a gene level Example: diabetes relevant gene ‚TCF7L2’ match path=(g:Gene{sid:'TCF7L2'})-[:MAPS|SYNONYM*0..2]-(g1:Gene) return path
- 21. Use case 2 Find information that is NOW connected Knowledge Graph
- 22. Query for SNPs (mutations) associated to diabetes Output: relevant protein and its function (ontology terms) match (tr:Trait) where tr.name contains ‚diabetes mellitus‘ with tr as disease match path=(disease)<-[:ASSOCIATED_WITH_TRAIT]-(asso:Association)<-[:SNP_HAS_ASSOCIATION]-(snp:SNP)- [:SNP_HAS_GENE]-(gene:Gene)-[:MAPS]-(g1:Gene)-[x:CODES]->(transcript:Transcript)-[:CODES]-> (prot:Protein)-[:ASSOCIATION]->(term:Term)—(o:Ontology) return path
- 23. Use case 3 Using graph algorithms to infer new insights Natural Language Processing Ontologies Knowledge Graph
- 24. Google’s page rank algorithm - find the most relevant gene finding ACE2 - the receptor the SARS-Cov2 virus uses to enter the cell • 140’000 abstracts from Covid19 related publications • NamedEntityRecognition of gene names • Page Rank identified ‚ACE2‘ as the most relevant gene
- 25. Who’s this ACE2-guy? source: https://www.benaroyaresearch.org/blog/post/11-things-know-about-mrna-vaccines-covid-19
- 26. Use case 4 Using node embeddings to sub phenotype diabetic patients Natural
- 27. DZDconnect connect raw data of diabetic patients with cancer Clinical data from 404 diabetic patients
- 28. DZDconnect connect lipidomics fingerprint Lipidomics Lipidomics experiment with 116 specific lipids
- 29. DZDconnect connect transcriptomics fingerprint Transcriptomics experiment with 58’345 specific Transcripts (RNAs)
- 30. Transform patients Fast random projections (fastRP) CALL gds.fastRP.write ( 'patients' , { embeddingDimension: 50 , writeProperty: 'fastrp- embedding' } ) YIELD nodePropertiesWritten Lipido
- 31. k-nearest neighbour clustering with k=5 representing the 5 diabetes subtypes patient 01 patient 02 patient 03 Graph algorithms patient 04 patient 05 patient 02 p a t i e n t 0 4 patient 03 patient 05 patient 01 subphenotyping of diabetic patients
- 32. DZDconnect connect patient data with knowledge graph Transcript Gene Synonyms Abstract PubMed Article Keyword MeSH-term Ontology term Hello role-model :-)
- 33. Take home message • Knowledge graph • as single point of truth • connect in-house data • scalability • infer new insights • Use cases: • simple and advanced (Cypher) queries • Graph Data Science library (page rank, kNN) • Node embeddings for complex data • NLP • Visualization of graph • different users • flask app, browser, SemSpect,…
- 34. Thanks to