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Knowledge management for integrative omics data analysis

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Knowledge management for integrative omics data analysis

  1. 1. Knowledge Management for Integrative Omics Data Analysis Barcelona 15.02.13 Dr. Hilmar Ilgenfritz Biomax Informatics AGwww.biomax.com
  2. 2. Biomax – Unique disruptive technology Biomax Profile Biomax VisionHeadquartered near Munich Master scientific complexityGermany Reduce cost and timeIn business for more than 15 Ensure ease of useyears Increase speed of developmentWorld wide customer base BioXM is a configurable Enable centers of excellence for knowledge management platform personalized medicine to flexibly interconnect isolated Support for Systems Biology silos of information in biomedical research
  3. 3. Generate scientific impact from knowledge exploitation Challenge Actionable to bridge knowledge for the gap rational decision making Insight withAggregate scientific impactCollect
  4. 4. ... e.g. expression data in a pathway context
  5. 5. Why „Knowledge Management“? Knowledge: “the realisation and understanding of patterns and their implications existing in information” Need to mine information for patterns A pattern often only emerges when information from different silos is combined e.g. Expression with gene function, SNPs with clinical history of patients, ... Need semantically integrated information e.g. Information about identical or “equivalent” objects and “meaning” requires framework for integration methods to find “equivalent” “meaning”
  6. 6. Knowledge Management aspects• Data integration• Semantic mapping• Knowledge representation• Data analysis• Knowledge extraction• Collaboration and project management
  7. 7. Public knowledge integration
  8. 8. Knowledge aspects in Systems Medicine Ontologies/SOPs/Study Design Clinical Biobank Data eCRF/ Experimental EHR Data PublicLiterature KM Molecular Data Data Data integration
  9. 9. Multi-Scale Modelling OrgansCells Individuals Tissues requires “language” for formal, structured description >> semantic mapping
  10. 10. Semantic mappingMapping entities or descriptions e.g. genes, phenotypescancer - blastoma, model parameters etc. with “equivalentmeaning” from different sources using controlled, structuredvocabularies drop dead drd, Q8IR42, AAD52607
  11. 11. Traditional semantic mapping KEGGPubMed Gene Ontology UniProt
  12. 12. Working with semantic networks• Connected data, meta- data and knowledge• Query, view, report• Integrate with analysis
  13. 13. Machine is configured to deliver relevant actionable BioXM knowledge through apps Machine is configured to build the connections to information Knowledge and data, based on the Model knowledge model Any type of data Any format of data Any volume of data Any location of data Any size of dataDocuments Spread sheets
  14. 14. Apps Common usersKnowledge Model Power userInformation and Data Access Administrator
  15. 15. Concept - Agile Solution Building Step 1: Specification • Designing the data modelQuery the knowledge network, explore Define the domain-specific data the graph and report query results model Step 3: Use Step 2: • Query building Implementation and information • Importing Instantiate the retrieval information knowledge network with data and information from external resources
  16. 16. Solution deployment Step 4 Web Apps for Information Retrieval, Reporting and Annotation
  17. 17. Sketch a domain model describing the semantic network
  18. 18. Setting up the data model - graphical support for modeling semantic networksExample:sub-network defininga study design
  19. 19. Import data into semantic structure
  20. 20. Knowledge Network Representation Dynamic network representation in BioXM Each node or edge of the network may serve as entry point for further exploration!
  21. 21. Knowledge Network Expansion Dynamic network representation in BioXM
  22. 22. On-the-fly queries retrievecontext-specific sub-network Experimental Knowledge data Molecular data Q u e r y e.g. Patient overview
  23. 23. Natural language query wizard
  24. 24. Explore network
  25. 25. Application areasClinical Research BiobankingPathway Analysis Literature Mining
  26. 26. Application areasNextGenSequencing Comparative Genomics Urban/rural ozone levels (annual average) Systems Biology Environmental Sciences
  27. 27. Knowledge Management aspects• Data integration• Semantic mapping• Knowledge representation• Data analysis• Knowledge extraction• Collaboration and project management
  28. 28. Connecting different sourcesExample:Literature derivedtissue specific FASpathway + CTD
  29. 29. Connecting different sourcesExample:Literature derivedtissue specific FASpathway + CTD
  30. 30. Validation of interconnection CASP10 - Tetrachlorethene, evidence from CTD 3472561
  31. 31. Another example
  32. 32. Knowledge Management aspects• Data integration• Semantic mapping• Knowledge representation• Data analysis• Knowledge extraction• Collaboration and project management
  33. 33. Multi-Scale Modelling
  34. 34. Structuring domain knowledge
  35. 35. Formalising domain knowledge
  36. 36. Extending domain knowledge
  37. 37. Navigate the network (find associated pathways)
  38. 38. Enter neighboring knowledge domain (Toll-like receptor signalling pathway)
  39. 39. Collect all information about IL6
  40. 40. Knowledge Management aspects• Data integration• Semantic mapping• Knowledge representation• Data analysis  Multi-variate data analysis• Knowledge extraction• Collaboration and project management
  41. 41. Complexity of chronic diseasesSocio- Lifestyle-environmenteconomic Risk and protective factorsdeterminants Tobacco smoking, pollutants, allergens, nutrition, infections, physical exercise, others Gender Genes, Cells, Tissues, Organs Biological expression of chronic diseases Transcripts, proteins, metabolites, Target organ local inflammation, Systemic inflammation Age Cell and tissue remodeling Clinical expression of chronic diseases Co-morbidities, Severity of co-morbidities, Persistence remission, Long-term morbidity, Responsiveness - side effects to treatment
  42. 42. The unmet need – transform data into insightInsight in aggregated clinical data for patient stratification in chronic disease • Up to 6,000 parameters per patient • 5 years of patient history Disperse clinical records
  43. 43. Oversight and insight over the clinical processes
  44. 44. Patient map – parameters for outcome prediction Specific outcome group accumulates in certain areas of the map
  45. 45. Patient group differentiating outcome profile Out of almost XXX differentiating attributes at high confidence X attributes constitute a robust predictor
  46. 46. Reliable outcome prediction validation Patients with outcome risk can be predicted with high reliability.
  47. 47. Knowledge Management aspects• Data integration• Semantic mapping• Knowledge representation• Data analysis  Integrative analysis• Knowledge extraction• Collaboration and project management
  48. 48. COPD ROS hypothesisMuscle wasting in COPD patients is effect of systemic inflammationresulting in nitroso-redox imbalance by mitochondria respiratory chain uncoupling in COPD patients with low body mass index
  49. 49. Metabolism/ROS-production ODE model linked with clinical data (Selivanov, Cascante, Barcelona) Biophysical J. 92, 3492-3500 Glycolysis J. Theor. Biol. 252, 402-410 Bioinformatics 22, 2806-2812 Clinical Data connection NAD Glc BMC Neuroscience, 7,(Suppl 1):S7 Mitochondria O2 uptake Exhalates TCA cycle Cit from Biobridge NADH Pyr AcCoA NAD OAA NADH Succ ADP NAD Lac Omics in O Blood RESPIRATION 2 transportc La ROS, ATP glutathione etc… CrP antioxidant systemClinical Data connection ROS cell damage”OMICS” in muscle biopsies from Biobridge(nitroso-redox balance, proteomics, genomics, signallingInflamation markers …)
  50. 50. Probabilistic network connecting inflammation and metabolism baseds on omics data (Turan, Falciani, Birmingham) PLoS Comput Biol. 7 e1002129
  51. 51. Extending the deterministic model Glycolysis NAD GlcClinical ADP Resulting connecting network data mechanic Myofibrils Glycolysis work ATP TCA cycle Cit NAD Glc NADH Pyr AcCoA NAD ADP OAA NADH Succ mechanic work ADP ATP O2 NAD Lac NADH Pyr transport Electron chain CrP ATP diffusion CrP ROS NAD Lac Deterministic models COPD knowledge base ATP Data clinical/ CrP experimental Selection of hubs Oxidative phosphorylation TCA COPD KB Cycle network Glycolysis search Probabilistic network Physiological measurments
  52. 52. Integrative prediction models ROS model gas exchangeO IO O lung heterogeneities I IO IO O I I I IO O IO KM KM I I KM Simulation environment clinical data BioBridge PAC-COPD ECLIPSE
  53. 53. Cross-study parameter matching
  54. 54. Semantic description of parameters
  55. 55. Mapping of model parameters Context:Parameter Description:Instance_B Ontology:A:54645 Element:Parameter:Instance_BOntology:A:5461 Ontology:B:987723 Ontology:C:21365 Ontology:A:54632 Element:Compound:Oxygen Element:Model Parameter:Instance_A Context:Model Parameter Description:Instance_A
  56. 56. Development of new probabilistic Glycolysis NAD Glc network from COPD KBhetero ADPgenic mechanic Myofibrils work ATP TCA cycle Cit NADH Pyr AcCoA Probabilistic NAD (predictive) OAA NADH Succ network ADP NAD Lac CrP Electron chain 0.1 0.3 ATP 0.4 0.6 O2 diffusion transport 0.8 CrP ROS 0.5 0.7 Deterministic models COPD knowledge base 0.2 0.4 0.1 Data clinical/ experimental Selection of hubs Oxidative phosphorylation TCA Cycle Resulting connecting network Glycolysis COPD KB Physiological Correlation network measurments network search
  57. 57. Hidden variable prediction ROS model gas exchange O I O O lung heterogeneities I I O I O O I I I I O O I O KM KM KM I I Simulation environment clinical data BioBridge PAC-COPD ECLIPSEProbabilistic model 0.1 0.3 0.4 0.6 0.8 0.5 0.7 0.2 0.4 0.1
  58. 58. Knowledge Management aspects• Data integration• Semantic mapping• Knowledge representation• Data analysis  Statistics examples• Knowledge extraction• Collaboration and project management
  59. 59. Principal Component Analysis of Functional Modules(activity of tissue remodelling pathways is altered in COPD patients)
  60. 60. Overrepresentation Analysis
  61. 61. Differential expression analysis
  62. 62. Cluster analysisn = 92 n = 200
  63. 63. Knowledge Management aspects• Data integration• Semantic mapping• Knowledge representation• Data analysis  Network search• Knowledge extraction• Collaboration and project management
  64. 64. Network analysis configuration
  65. 65. Network Search ResultsResulting connectingonnecting network between two sources objects
  66. 66. Knowledge Management aspects• Data integration• Semantic mapping• Knowledge representation• Data analysis• Knowledge extraction• Collaboration and project management
  67. 67. Systematic literature review
  68. 68. Review results: Web Input form
  69. 69. Searches supporting the review flow
  70. 70. Results Stage 3: "include"
  71. 71. Thank you !

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