Presentation on using rules - based text mining to extract protein interactions from the PubMed database.

1. Text Mining Using JBoss Rules with a BioMedical Example Mark Maslyn Consultant [email_address] To Be Presented 2/2/2010 at Denver Open Source User Group

3. What Are Proteins ? Chains of Amino Acids that fold into unique shapes that determine what other proteins will interact with them. Diagram From WikiMedia Commons

4. Two Proteins Binding Together Diagram From WikiMedia Commons

5. Interacting Proteins Form New Molecules + + Substrate Enzyme Enzyme Product

6. Protein Interactions Form Networks Start 1 st Level 2 nd Level From Bolouri (2008) – Used By Permission

7. Chemical Feedback Loop To Keep Glucose Concentration Constant Glucose ( Sugar ) Too Little Too Much Glycogen ( Fat ) Prot > Prot > Prot Prot < Prot < Prot

8. Finding Protein / Protein Interactions is the Holy Grail of Pharmacology They Can Lead to New Treatments Image From WikiMedia Commons

9. Where Do I Get the Data ?

10. The Problem is: There's Too Much Data 2,000 New References Every Day

15. Example Production Rule (BN Format) with Expected Order S p1 a p2 Where : p1 and p2 = different protein names (e.g. p53, BRCA1, etc) a = action verb (e.g. regulate, interact, modulate, bind, etc)

17. Text Mining Flow Chart Retrieve Parse Filter and TransformKeywords Rules to Evaluate Output

18. Cytoscape One Level Network Diagram Statistics: 200 References 7 Unique Links One Level Tree

19. Cytoscape Two Level Network Diagram Statistics: 1600 References 25 Unique Links Two Level Tree

20. Further Information Mark Maslyn: [email_address] http://www.slideshare.net/mmaslyn/text-mining-using-jboss-rules-2773851