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MINING FOR MEANING: Data mining & Knowledge extraction

This experiment involves data mining and knowledge extraction techniques to interpret experimental results for functional and gene expression profiling. The study focuses on high publication rates and utilizes high-throughput screening to identify relevant genes and their expression patterns. The experiment includes infection with 5 pathogens and analysis of gene expression profiles. The functional implications of the findings are determined using gene ontologies and literature profiling. This process involves co-citation networks, natural language processing, and literature clustering to analyze the abundance of relevant literature and identify functional relationships. The experiment aims to generate knowledge and conclusions from a dataset of 12 million references.

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MINING FOR MEANING: Data mining & Knowledge extraction

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  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

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

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

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

  7. MINING FOR MEANING: FUNCTIONAL PROFILING

  8. 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

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

  10. 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

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

  12. http://www.geneontology.org/

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

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

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

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

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

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

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

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

  21. http://www.pubgene.com/

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

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

  24. 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

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

  26. MINING FOR MEANING: FUNCTIONAL PROFILING LITERATURE MINING

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

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

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

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

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

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