Amit Sheth's Keynote at Semantic Web Technologies for Science and Engineering Workshop (held in conjunction with ISWC2003), Sanibel Island, FL, October 20, 2003.

6. What is difficult, tedious and time consuming now … What genes do we all have in common?* Research to answer this question took scientists two years** * G. Strobel and J Arnold. Essential Eukaryotic Core, Evolution (to appear, 2003) **but we now believe with semantic techniques and technology, we can answer similar questions much faster

7. Why? Bioinformatics, ca. 2002 Bioinformatics In the XXI Century From http://prometheus.frii.com/~gnat/tmp/stein_keynote.ppt

8. Science then , then and now In the beginning, there was thought and observation.

10. The achievements are still admirable … Reasoning and mostly passive observation were the main techniques in scientific research until recently. … as we can see

11. Science then, then and now A vast amount of information

12. Science then, then and now No single person, no group has an overview of what is known . Known, But not known …  not known

14. Science then, then and now

15. Science then, then and now Ontologies embody agreement among multiple parties and capture shared knowledge. Ontology is a powerful tool to help with communication, sharing and discovery. We are able to find relevant information ( semantic search/browsing ), connect knowledge and information ( semantic normalization/integration ), find relationships between pieces of knowledge from different fields ( gain insight, discover knowledge )

16. Intervention by Ontologies, some near future Bioinformatics In the XXI Century

24. Broad Scope of Semantic (Web) Technology Other dimensions: how agreements are reached, … Lots of Useful Semantic Technology (interoperability, Integration) Cf: Guarino, Gruber Gen. Purpose, Broad Based Scope of Agreement Task/ App Domain Industry Common Sense Degree of Agreement Informal Semi-Formal Formal Agreement About Data/ Info. Function Execution Qos Current Semantic Web Focus Semantic Web Processes

25. Knowledge Representation and Ontologies Catalog/ID General Logical constraints Terms/ glossary Thesauri “ narrower term” relation Formal is-a Frames (properties) Informal is-a Formal instance Value Restriction Disjointness, Inverse, part of… Ontology Dimensions After McGuinness and Finin Simple Taxonomies Expressive Ontologies Wordnet CYC RDF DAML OO DB Schema RDFS IEEE SUO OWL UMLS GO KEGG GlycO TAMBIS EcoCyc BioPAX

26. GlycO: Glycan Structure Ontology UGA’s “Bioinformatics for Glycan Expression” proj. Not just Schema/Description (partial view shown), also description base/ontology population. In progress, uses OWL.

29. Existing Systems using Semantics for Bioinformatics FOCUS: SEMANTIC SEARCH AND BROWSING (with nascent work in discovery)

30. Recent Articles Experts Organizations Metabolic Pathways Protein Families Proteins Genes Related Diseases

31. Semagix Freedom Architecture (a platform for building ontology-driven information system) Ontology © Semagix, Inc. Content Sources Semi- Structured CA Content Agents Structured Unstructured Documents Reports XML/Feeds Websites Email Databases CA CA Knowledge Sources KA KS KS KA KA KS Knowledge Agents KS Metabase Semantic Enhancement Server Entity Extraction, Enhanced Metadata, Automatic Classification Semantic Query Server Ontology and Metabase Main Memory Index Metadata adapter Metadata adapter Existing Applications ECM EIP CRM

33. 1. Ontology Model Creation (Description) 2. Knowledge Agent Creation 3. Automatic aggregation of Knowledge 4. Querying the Ontology Ontology Creation and Maintenance Steps © Semagix, Inc. Ontology Semantic Query Server

35. Cerebra’s myGrid Framework

37. Applying Semantics to BioInformatics : Example 1 Semantic Browsing, Querying and Integration

38. Present: User queries multiple sources Heterogeneous data sources on the web ?

39. Future: the Web-Service queries multiple sources

40. Semantic Querying, Browsing, Integration to find potential antifungal drug targets Databases for different organisms Is this or similar gene in other organism? (most Antifungals are associated with Sterol mechanism ) Services: BLAST, Co-expression analysis, Phylogeny; If annotated, directly access DB, else use BLAST to normalize FGDB

41. Applying Semantics to BioInformatics : Example 2 Analytics in Drug Discovery

47. Applying Semantics to BioInformatics : Example 3 Using Ontologies in cancer research

48. Disparate Data from Different Experiments Metastatic cancer cells Increased GNT-V Activity Experiment 1 Experiment 2 Cancer marker glycan sequence elevated in glycoprotein beta 1 integrin

51. Applying Semantics to BioInformatics : Example 4 Applying Semantics to BioInformatics Processes

55. Semantic Bioinformatics Processes SIMILAR SEQUENCES MATHCER GO id MULTIPLE SEQUENCE ALIGNMENT PHYLOGEN TREE CREATOR FUNCTIONAL SEMANTICS Use Functional Ontologies for finding relevant services Sequence Matcher ENTREZ FETCH LOOKUP Sequence Alignment CLUSTAL MEME Phylogen Tree Creator PAUP PHYLIP TREEVIEW

56. Semantic Bioinformatics Processes SIMILAR SEQUENCES MATHCER GO id MULTIPLE SEQUENCE ALIGNMENT PHYLOGEN TREE CREATOR Use QoS Ontologies to make a choice Sequence Matcher ENTREZ FETCH QOS Time X secs Reliability X % QoS SEMANTICS QOS Time X secs Reliability X %

57. Semantic Bioinformatics Processes GO id DATA SEMANTICS Use Concept Ontologies for Interoperability MAY REQUIRE DATA CONVERSION MAY REQUIRE DATA CONVERSION SIMILAR SEQUENCES MATHCER MULTIPLE SEQUENCE ALIGNMENT PHYLOGEN TREE CREATOR

58. Semantic Bioinformatics Processes GO id CLUSTAL PAUP EXECUTION SEMANTICS Use Execution Semantics for execution monitoring of different instances GO id MEME PHYLIP GO id CLUSTAL PAUP FETCH ENTREZ ENTREZ

59. Semantic Web Process Design Template Construction

60. Common genes