Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
0 likes363 views
The document describes Qiagen's biomedical knowledge graph, which provides extensive data for data scientists and bioinformaticians in the field of molecular biology. It highlights the capabilities of curated and AI-derived datasets, allowing users to explore biomedical relationships and conduct analytics-driven drug discovery. Qiagen's offerings include tools for disease and drug intelligence, target identification, and the integration of various data sources to facilitate research and discovery.
![What genes cause or correlate with asthma?
30
Genes
Diseases
match (d:disease {name: 'Asthma'})<-[r:C|CO]-(g0:gene)
where any (
subtype_list in g0.node_subtype
where subtype_list in [
'enzyme', 'transcription regulator', 'transporter',
'kinase', 'G-protein coupled receptor', 'peptidase',
'transmembrane receptor', 'ion channel', 'phosphatase',
'translation regulator', 'cytokine', 'growth factor',
'ligand-dependent nuclear receptor'])
return d, r, g0
355 nodes
8309 relationships](https://image.slidesharecdn.com/munichpharmajune12-240617121801-2507adcd/85/Biomedical-Knowledge-Graphs-for-Data-Scientists-and-Bioinformaticians-30-320.jpg)
![Genes
Tox Functions
Pathways
How are asthma-related genes functionally linked?
31
...
optional match (g0:gene)-[:is_a*]->(g1:gene {macromolecule_level: 'ortholog group level'})
optional match (g1:gene {macromolecule_level: 'ortholog group level'})-[r1:member_of]-(p:pathway|toxlist)
with p, collect(distinct g1) as genes, collect(r1) as relationships
where size(genes) >= 2
return genes, relationships, p
281 genes
426 pathways
62 toxlists
3507 relationships](https://image.slidesharecdn.com/munichpharmajune12-240617121801-2507adcd/85/Biomedical-Knowledge-Graphs-for-Data-Scientists-and-Bioinformaticians-31-320.jpg)
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
- 1. Biomedical KB-HD/AI: Biomedical Knowledge Graph for Data Scientists and Bioinformaticians Dmitrii Kamaev PhD
- 2. Legal disclaimer QIAGEN products shown here are intended for molecular biology applications. These products are not intended for the diagnosis, prevention or treatment of a disease. For up-to-date licensing information and product-specific disclaimers, see the respective QIAGEN kit instructions for use or user operator manual. QIAGEN instructions for use and user manuals are available at www.qiagen.com or can be requested from QIAGEN Technical Services (or your local distributor). 2
- 3. QIAGEN Digital Insights (QDI) Leading provider of genomic and clinical knowledge, analysis and interpretation tools and services for scientists and clinicians 3 Powered by the acquisition of: …one of 3 Business Units within QIAGEN 3,000,000
- 4. QIAGEN Discovery Insights: leading provider of expert-curated knowledge 4 June 14, 2024 Curated research findings Highlight pathways, map networks, discover mechanisms of action Curated ‘omics data Search across diseases and tissues, find comparisons, identify biomarkers Curated gene variants Somatic or germline compendiums, observed clinical case distribution
- 5. Applications Quickly and efficiently generate novel, high-quality discoveries through highly flexible data analysis and exploration 5 Analytics-driven drug discovery Build applications Integrate Combine our leading data with your innovative analysis approaches and a wide range of advanced algorithms developed by the industry to power analytics and AI-driven drug discovery Use the data within your own analysis and data-exploration applications Integrate the data with other data types and sources, as well as third-party technologies. Can act as a foundational data model. Primary application categories: Biomedical knowledge graph construction and analysis Most popular applications Analytics and AI-driven target identification and drug repositioning Target, disease and drug intelligence portals Disease subtype and biomarker identification based on functional features
- 6. QIAGEN Biomedical Knowledge Base Break knowledge silos to power R&D with data science 6 Biomedical KB-HD (human-derived) • Manually curated by expert scientists • Contains over 24 million biomedical relationships Biomedical KB-AI (generative AI-derived) • Curated through advanced AI processes • Boasts 600 million+ biomedical relationships • Quarterly updates • Available as flat files, knowledge graphs, APIs • FAIR friendly • Foundational data model that can scale Saving time and facilitating research with comprehensive databases.
- 7. Many ways to access QIAGEN-curated relationships 7 94,000 diseases Downloadable flat files Python, R, and REST APIs Causal analysis and export functions Neo4j and SQL database imports PubMed TargetScan BioGRID UMLS SnoMed MeSH FDA, ClinVar ClinicalTrials.gov DrugBank 17,000 drugs 51,000 functions 49,000 chemicals 20 M research findings
- 8. Tabular representation VS graph representation June 14, 2024 8 Relationships Entity metadata
- 9. Tabular representation June 14, 2024 9 Tabular representation makes many queries complex to write
- 11. Real schema and real data June 14, 2024 11
- 12. Knowledge Graph Schema Design June 14, 2024 12 Completeness Simplicity Better performance Increases adoption Supports diverse user needs Scientific thoroughness
- 13. Design Choices: Gene Representation June 14, 2024 13
- 14. Design Choices: non-directional relationships as a single relationship June 14, 2024 14 Avoids the problem of deduplicating relationships afterwards Protein-Protein interaction has no directionality. How should we represent it?
- 15. Design Choices: Clinical Trial Fine-Grained Representation June 14, 2024 15 Shi, X., Du, J. Constructing a finer-grained representation of clinical trial results from ClinicalTrials.gov. Sci Data 11, 41 (2024). https://doi.org/10.1038/s41597-023-02869-7
- 16. Design Choices: Clinical Trial Evidence Representation June 14, 2024 16 Evidence Drug Drug Target Disease Evidence attributes
- 17. Design choices: relationship aggregation June 14, 2024 17
- 18. Design Choices: Roll up of relationships in ontologies June 14, 2024 18
- 19. Graph Customization: Build Your Own Graph • Custom names of nodes and relationships • Customization of attributes • Aggregation of edges • Subgraph centered around a certain node • Exclude irrelevant portions of the content June 14, 2024 19
- 20. June 14, 2024 20 Good schema design is a balance between simplicity and comprehensiveness
- 21. Recursive queries: finding positive feedback loops June 14, 2024 21
- 22. Querying ontologies June 14, 2024 22 Equivalent SQL:
- 23. Hop 1 and 2 expression networks of ANO1 June 14, 2024 23
- 24. Clustering of core pathways in 3D June 14, 2024 24
- 25. Library for 3D visualization June 14, 2024 25 https://github.com/vasturiano/3d-force-graph
- 26. SemSpect: Data Exploration Plugin for Neo4j June 14, 2024 26
- 27. SemSpect: Avoid hairballs in your exploration June 14, 2024 27 Hides complexity in tables
- 28. Relationship-Based Constraints June 14, 2024 28
- 29. June 14, 2024 29 Graph representation enables discovery through exploration within the complex interconnections of biomedical data
- 30. What genes cause or correlate with asthma? 30 Genes Diseases match (d:disease {name: 'Asthma'})<-[r:C|CO]-(g0:gene) where any ( subtype_list in g0.node_subtype where subtype_list in [ 'enzyme', 'transcription regulator', 'transporter', 'kinase', 'G-protein coupled receptor', 'peptidase', 'transmembrane receptor', 'ion channel', 'phosphatase', 'translation regulator', 'cytokine', 'growth factor', 'ligand-dependent nuclear receptor']) return d, r, g0 355 nodes 8309 relationships
- 31. Genes Tox Functions Pathways How are asthma-related genes functionally linked? 31 ... optional match (g0:gene)-[:is_a*]->(g1:gene {macromolecule_level: 'ortholog group level'}) optional match (g1:gene {macromolecule_level: 'ortholog group level'})-[r1:member_of]-(p:pathway|toxlist) with p, collect(distinct g1) as genes, collect(r1) as relationships where size(genes) >= 2 return genes, relationships, p 281 genes 426 pathways 62 toxlists 3507 relationships
- 32. Genes Tox Functions Pathways 32 Louvain neighborhood detection, then filtering by centrality Can we use biological activity to identify functional neighborhoods?
- 33. 33 Drugs known to activate or inhibit Can we repurpose drugs to target key intersections? Genes Tox Functions Drugs Pathways Immunosuppressant approved for atopic dermatitis Phase two complete for asthma
- 34. Link Prediction June 14, 2024 34 Complex Embeddings for Simple Link Prediction, Theo Trouillon et al. Gene Disease ? Define train/test split using Neo4j •Taking random links between gene-disease •Mark links to child and parent diseases as exclude Trained ComplEx embeddings with DGL-KE Compared to predictions based on node degree
- 35. QIAGEN Biomedical KB-AI: provides the greatest depth and breadth of knowledge for critical pharmaceutical research Unstructured relationship sources Structured relationship sources Graph enrichment sources 35 NIH PMC PubMed arXiv medRxiv bioRxiv Google Patents GWAS Catalog dbSNP ChEMBL RxNav CPDB ClinicalTrials.gov un1Chem PubChem FDA HGNC reactome GENEONTOLOGY MeSH Open Targets UniProt DAILYMED 12 billion+ triples; 600 million+ relationships • 335 million+ relationships from scientific research • 9.4 million+ relationships from patents • 14.9 million+ relationships from grants • 4.7 million+ relationships from clinical trials • 279 million+ relationships from structured sources Discovery Identify new targets and indications with genetic evidence found across scientific literature Clinical development Establish potential biomarkers for diseases Business development and strategy Understand the competitive landscape by target, drug, indication and augment scientific due diligence Data generated using state-of-the-art entity disambiguation, semantically meaningful relationship extraction and causal relationships.
- 36. June 14, 2024 36 Entities and Relationships in Biomedical KB-AI Relationships Semantic: 290 million Causal: 9 million Adverse effects: 280 million Clinical Trials: 4.7 million GWAS: 2 million
- 37. Preclinical Competitive Intelligence June 14, 2024 37 Biomedical KB-AI provides many competitive intelligence sources including • Patents • Clinical trials • Research papers • Grant applications GLP1R patent mentions
- 38. Preclinical Competitive Intelligence June 14, 2024 38 Biomedical KB-AI provides many competitive intelligence sources including • Patents • Clinical trials • Research papers • Grant applications Top 20% clinical trial sponsors for GLP1
- 39. Indications GLP1 Is Investigated For By Top Pharma Companies
- 40. Timeline – Top 4 drugs targeting GLP1R Evidence comes form NIH grants, Publicaitons and Patents Evidence accumulation for GLP1R interacting drugs
- 41. Rare diseases research 6/14/2024 41 Hypophosphatasia and Ehlers-Danlos syndrome • Building chat interface model for HPP and EDS scientific publications • Building model that augments research for HPP and EDS
- 42. June 14, 2024 42 Graph representation supports complex analyses of biomedical data
- 43. June 14, 2024 43 • Kyle Nilson • Millie Zhou • Ivana Grbesa • Francesco Lamanna • Andreas Kramer • Bob Rebres • Burk Braun • Swati Mishra • Bjarke Skjernaa • Allan Merrild • Rune Gee Madsen • Poul Liboriussen • Thomas Hyldgaard • Venkatesh Moktali • Alex Jarasch • Alexander Erdl • Vincent Vialard Acknowledgments
- 44. Thank you for your attention Trademarks: QIAGEN®, Sample to Insight®, Ingenuity®, IPA® (QIAGEN Group). Registered names, trademarks, etc. used in this document, even when not specifically marked as such, may still be protected by law. PROM-21134-001 © 2022 QIAGEN, all rights reserved.