Life Sciences Integrated Demo Joyce Peng Senior Product Manager, Life Sciences Oracle Corporation

2. Life Sciences Integrated Demo Joyce Peng Senior Product Manager, Life Sciences Oracle Corporation Yao-chun.Peng@oracle.com

3. Manage vast quantities of data Informatics Challenges Accessheterogeneous data Access heterogeneous Data Collaborate securely Integrate a variety of data types Find Patterns and insights

4. Oracle Life Sciences Platform Transparent Gateways Fast access using Oracle OCI Distributed Queries Perform searches across domains Generic Gateways Access any data using ODBC e.g. MySQL GenBank e.g. PubMed External Tables Ability to index and query external files UltraSearch Search external sites & repositories MySQL Toolkit Easily move MySQL data into Oracle Real Application Clusters Linear scalability Oracle Portal Build personalized portals Application Server Provide scalability for themiddle tier XML DB Flexibly manage data interMedia Store & manage images Security Enforce security Auditing Create audit trail to facilitate FDA compliance Workflow Automate laboratory & business processes Collaboration Suite Collaborate securely iFS/Files Share documents e.g. SwissProt SP-ML Data Mining Discover patterns & insights BLAST Sequence similarity search Network Model Pathways Modeling Statistics Perform basic statistics Table Functions Implement complex algorithms OLAP & Discoverer Interactive query & drill-down Extensibility Framework (Data cartridges), manage complex scientific dataLOBs Manage unstructured data Text Index & query text, e.g. literature searches SQL Loader High performance data loader Web Services Standard communication between applications Merge/Upsert Enabling update and insert in one step TransportableTablespaces Rapidly exchange tables Oracle Streams Rule-based subscription for information sharing

5. Platform Features Highlighted Transparent Gateways Fast access using Oracle OCI Distributed Queries Perform searches across domains Generic Gateways Access any data using ODBC e.g. MySQL GenBank e.g. PubMed External Tables Ability to index and query external files UltraSearch Search external sites & repositories MySQL Toolkit Easily move MySQL data into Oracle Real Application Clusters Linear scalability Oracle Portal Build personalized portals Application Server Provide scalability for themiddle tier XML DB Flexibly manage data interMedia Store & manage images Security Enforce security Auditing Create audit trail to facilitate FDA compliance Workflow Automate laboratory & business processes Collaboration Suite Collaborate securely iFS/Files Share documents e.g. SwissProt SP-ML Data Mining Discover patterns & insights BLAST Sequence similarity search Network Model Pathways Modeling Statistics Perform basic statistics Table Functions Implement complex algorithms OLAP & Discoverer Interactive query & drill-down Extensibility Framework (Data cartridges), manage complex scientific dataLOBs Manage unstructured data Text Index & query text, e.g. literature searches SQL Loader High performance data loader Web Services Standard communication between applications Merge/Upsert Enabling update and insert in one step TransportableTablespaces Rapidly exchange tables Oracle Streams Rule-based subscription for information sharing

6. BioOracle Project We are scientists at a life sciences company looking to find a cure for Lymphoma

7. BioOracle Portal Integrated data view and Single-Sign-On to many applications

8. Find a Cure for Lymphoma • Literature search on Lymphoma • Set up a project workspace • Set up a meeting • Check lab protocols • Store cell histology images • Analyze gene expression results • Study the markers • Find a lead

9. Literature Search Search document content.

10. Extract Document Themes

11. Generate the Gist

12. Categorize Documents

13. Text Mining

14. Find a Cure for Lymphoma • Literature search on Lymphoma • Set up a project workspace • Set up a meeting • Check lab protocols • Store cell histology images • Analyze gene expression results • Study the markers • Find a lead

15. BioOracle Project In Oracle Files Lymphoma project workspace after adding documents

16. BioOracle Project in Oracle Files Support revision control

17. BioOracle Project in Oracle Files Associate metadata (Categories) to a document.

18. BioOracle Project in Oracle Files Advanced Search

19. Approval Workflow

20. Approval Workflow

21. BioOracle Project in Oracle Files Access Control

22. BioOracle Project in Oracle Files • Support • HTTP/WebDAV(Web) • SMB (Windows) • NFS (UNIX) • AFP (Apple Mac) • FTP protocols

23. Wireless Access

24. Highly Scalable, Worldwide Access

25. Find a Cure for Lymphoma • Literature search on Lymphoma • Set up a project workspace • Set up a meeting • Check lab protocols • Store cell histology images • Analyze gene expression results • Study the markers • Find a lead

26. Calendar Use calendar in Collaboration Suite to schedule meetings with collaborators

27. Internet Meeting

28. Protocol Sharing

29. Find a Cure for Lymphoma • Literature search on Lymphoma • Set up a project workspace • Set up a meeting • Check lab protocols • Store cell histology images • Analyze gene expression results • Study the markers • Find a lead

30. BioOracle Image Management Use interMedia to manage and query Lymphoma histology data

31. BioOracle Image Management Generate image thumbnails

32. BioOracle Image Management Integrated search across relational data and image attributes extracted

33. DLBC Follicular Gene Expression Analysis for Lymphoma Biopsies Samples Feature Selection SQL Oracle Data Mining Feature Selection Molecular Pattern Recognition Oracle Data Mining Bayesian Classifier Interpretation of Results Discoverer Reports Portals Java Servlets Filtering and Pre-Processing SQL, XML, Java Instruments Affymetrix Microarray Use analytical pipeline to identify the patterns that differentiate DLBC from Follicular Lymphoma Prediction: DLBC Follicular Dataset from Golub et al Science 286:531-537.

34. Find a Cure for Lymphoma • Literature search on Lymphoma • Set up a project workspace • Set up a meeting • Check lab protocols • Store cell histology images • Analyze gene expression results • Study the markers • Find a lead

35. Oracle Data MiningClassification of Cancer Subtypes (DLBC versus Follicular) Oracle provides wizards to guide analysts through data mining model creation

36. Oracle Data Mining Build a classification model

37. Oracle Data Mining Select the target field, e.g. DLBC or Follicular Lymphoma

38. Oracle Data Mining Select the classification model

39. Oracle Data Mining Test the model on the data set of interest

40. Naïve Bayes has built a model that distinguishes DLBC from Folicular with 77% accuracy The confusion matrix shows the number of times the model’s predictions are accurate

41. Oracle Data Mining See if the Adaptive Bayes Network algorithm can build a better model

42. Oracle Data Mining Use wizards to define parameters for building a model

43. Oracle Data Mining Adaptive Bayes Network algorithm can predict Lymphoma subtype with 84% accuracy

44. Oracle Data Mining Adaptive Bayes Network algorithm generates rules for model interpretation

45. Oracle Data Mining in JDeveloper Automatically create the Java code needed to build analytical pipelines inside the database