MINING FOR MEANING: Data mining & Knowledge extraction

1. Laboratory of Parasitic Diseases, NIAID MINING FOR MEANING:Data mining &Knowledge extraction

2. Experiment Results Knowledge Conclusions Data interpretation

3. Data interpretation Experiment Results Knowledge Knowledge • High publication rates Conclusions

5. Results • High-throughput screening Data interpretation Experiment Results Knowledge Knowledge • High publication rates Conclusions

6. Data interpretation Experiment GENE EXPRESSION PROFILING Results Results Identify relevant genes Identify expression patterns Knowledge Knowledge Conclusions

7. Data interpretation Experiment GENE EXPRESSION PROFILING Results Identify relevant genes Identify expression patterns Knowledge Knowledge FUNCTIONAL PROFILING Identify functional implications Conclusions

8. MINING FOR MEANING: FUNCTIONAL PROFILING

9. Infection with 5 pathogens -overnight- IL-4 + GM-CSF DC RNA pools M-CSF Elutriated Human Monocytes U95 Mac 7 donors Intracellular Extracellular vs Protozoan vs Bacteria Brugia malayi Toxoplasma gondii Leishmania Mycobacterium tuberculosis Leishmania major vs Leishmania donovani

10. DC Mac Gene expression profiling Dataset Dataset  Extraction Filter  1200 75 12.000 genes

11. Induced by Bm 50 DC Bm 5 Mtb Lm Ld Tg Pathogens intracellular pathogens Fold Change (log2) Fold Change (log2) Leishmania & TB Genes Toxoplasma & TB Fold Change (log2) Fold Change (log2) Toxoplasma

13. MINING FOR MEANING: FUNCTIONAL PROFILING WITH GENE ONTOLOGIES - GO

14. http://www.geneontology.org/

15. http://apps1.niaid.nih.gov/david/

19. MINING FOR MEANING: FUNCTIONAL PROFILING WITH GENE ONTOLOGIES - GO WITH LITERATURE ONTOLOGIES - MESH

20. http://www.nlm.nih.gov/mesh/MBrowser.html

21. http://array.ucsd.edu/hapi/ http://132.239.155.52/HAPI/TEST_377.HTML

23. MINING FOR MEANING: FUNCTIONAL PROFILING WITH GENE ONTOLOGIES - GO WITH LITERATURE ONTOLOGIES - MESH WITH LITERATURE ABSTRACTS

24. Data interpretation Experiment Results Knowledge Knowledge 12 million references Conclusions

25. Data interpretation Experiment Results Results Knowledge Knowledge Conclusions 12 million references

26. MINING FOR MEANING: FUNCTIONAL PROFILING WITH LITERATURE ABSTRACTS  Co-citation Network

27. http://www.pubgene.com/

28. MINING FOR MEANING: FUNCTIONAL PROFILING WITH LITERATURE ABSTRACTS  Co-citation Network Natural Language Processing

30. MINING FOR MEANING: FUNCTIONAL PROFILING WITH LITERATURE ABSTRACTS  Co-citation Network Natural Language Processing Literature Profiling

32. 1.Gene - Literature indexation Gene A Gene B Gene C… Gene X Abstracts 2. Analysis of abstract contents 3.Term filtering Term occurrences in abstracts Discrimination Co-occurrence Analyze functional relationships Retrieve relevant literature for each gene Determine term occurrence in abstracts Select relevant terms

33. KNOWLEDGE - Identify functional relationships - Translate genelists into keywords - Interpret data Experimental system Gene List 12 million references

36. MINING FOR MEANING: FUNCTIONAL PROFILING LITERATURE MINING

37. Data interpretation Experiment Results Knowledge Knowledge • High publication rates Conclusions

38. MINING FOR MEANING: DATA MINING LITERATURE MINING DOCUMENT CLUSTERING

40. MINING FOR MEANING: DATA MINING LITERATURE MINING DOCUMENT CLUSTERING NLP

42. MINING FOR MEANING: DATA MINING LITERATURE MINING DOCUMENT CLUSTERING NLP LITERATURE PROFILING

43. Data interpretation Experiment GENE EXPRESSION PROFILING Results Results Identify relevant genes Identify expression patterns Knowledge Knowledge Conclusions