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CD40 ligand and tumor necro sis factor alpha , the cells acquire a mature

CD40 ligand and tumor necro sis factor alpha , the cells acquire a mature phenotype of dendritic cells that is characterized by up - regulation of human leukocy te antigen ( CD80 , CD86 , CD40 and CD54 and appearance of CD83 . These. Erik van Mulligen Martijn Schuemie

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CD40 ligand and tumor necro sis factor alpha , the cells acquire a mature

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  1. CD40ligand and tumornecro sisfactoralpha, the cells acquire a mature phenotype of dendriticcells that is characterized by up-regulation of humanleukocy teantigen (CD80, CD86, CD40 and CD54 and appearance of CD83. These • Erik van Mulligen • Martijn Schuemie • Rob Jelier • Antoine Velthoven • Christina Hettne • Jan Kors • Johan van der Lei • Christine Chichester • Erik van Mulligen • Marc Weeber • Kevin Kalupsen • Reuben Christie • Jacintha van Beemen • Nickolas Barris • Albert Mons • Peter Bram ‘t Hoen • Ellen Sterrenburg • Herman van Haagen • Allessandro Botelho-Bovo • Judith Boer • Johan den Dunnen • Gert Jan van Ommen • Gerard Meijssen • Erik Moeller • Peter Jan Roes • Karsten Uil • Siebrand Mazeland • Sabine Cretella Barend Mons Second Order Semantic Enrichmentand the role of Wiki’s for Professionals

  2. The ConsortiumOpen Access Semantic Support TechnologyFor on-line Knowledge Tracking, Discovery and Management

  3. WikiProfessional Semantic Web workspaces for scientists enabling real time knowledge exchange and exploration

  4. Many challenges in current bomedical research Volume of data (both high troughput and text) Complexity Distributed systems and databases Incompatible data formats Multi-disciplinarity Multi-linguality Ambiguity of terminology Inability to share Knowledge Globalization of knowledge

  5. Repetition of facts is of great value for the readability of individual papers, • but the fact itself is a single unit of information, and needs no repetition.

  6. A defining characteristic of wiki technology is the ease with which pages can be created and updated. Generally, there is no review before modifications are accepted. The Million Minds Approach

  7. Websites such as www.dmd.nl are increasingly cited in the literature Personal Communication Johan den Dunnen.

  8. The majority of (SP) proteins has more than 1 research group asociated

  9. So…..can we use wikis for this ??????

  10. 2nd order S.E. The Knowlet First order semantic enrichment • Contextual annotation of web pages for interactive browsing, van Mulligen E, Diwersy M, Schijvenaars B, Weeber M, van der Eijk CC, Jelier R, Schuemie M, Kors J, Mons B, Medinfo 2004, 11:94-8 • Which gene did you mean?, Mons B, BMC Bioinformatics 2005 Jun 7, 6:142

  11. Knowlet building block Knowlet of core concept Knowlet space What does a Knowlet look like ‘under the hood’? <Source concept> <Target Concept> <Relations>: <Typea1> Database facts (mutiple attributes) <Typea2> Community Annotations (WikiProf) <Typeb1> Co-occurrence sentence <Typeb2> Co-occurrence abstract <Typec1> Concept Profile Match <Type c2> Sequence similarity (BLAST score Genes and Proteins only) <Type c3 Co-expression with (genes from expression Databases)

  12. Rules to combine different sources of information into a single relationship Time-stamped information The relationship to the original texts or database entries K D K E K D K G K A K G K H K D K H K Z K F K Z K B K I K B factual co occurrence associative

  13. A Knowlet represents a unit of thought interconnected with other units of thoughts or in other words: a ‘cloud’ of concepts that have one or more relationship types with the central (selected) concept The interconnection reflects a semantic relationship derived: From facts in database From co-occurrence in a text From other associations Relations have a strength Based on the source of relationship Based on the amount of «evidence» Knowlets belong to one or more semantic classes: proteins, diseases, authors, organizations, journals, experiments, etc. Each Knowlet is uniquely identified by a URL or URI (Unique Resource Identifier) The Knowlet

  14. 1 Million person organisation Object 1 Object 2 Object 3 drug disease 3. Building an association matrix of large data sources 1 Million gene

  15. SRP PARN l • Assignment of protein function and discovery of new nucleolar proteins based on automatic analysis of MEDLINE. • Martijn Schuemie, Christine Chichester, Frederique Lisaceck, Yohann Coute, Peter-Jan Roes, Jean Charles Sanchez, Barend Mons • Special issue on Systems Biology in Proteomics, 2008 (accepted for publication)

  16. Kappa-based clustering based on Gene ID Cluster 1: Mdx mice Dysferlin-deficient mice Cluster 2: myositis Cluster 3: DMD Cluster 4: EOM-specific genes in mdx Cluster 5: Development of EOM muscle and rat atrophy Cluster studies on basis of Homologene IDs GeneSet Clusterer, Rob Jelier, Erasmus MC

  17. Clustering of genes based on similarity of concept profiles Cluster 1: atrophy and myopathy Cluster 2: extraocular muscle of mdx Cluster 3: human and mouse muscular dystrophies and myositis Cluster 4: long gene lists Cluster 5: muscle differentiation; Ky-mutant and Fxr-/- mice Cluster 6: ageing and sarcopenia GeneSet Clusterer, Rob Jelier, Erasmus MC

  18. Annotate Many assocations on concept profile level Evaluate biological processes that bring studies together No overlap on GeneID level DatasetComparer, Rob Jelier, Erasmus MC

  19. OmegaWiki (terminology system) • Wiki Authors • Wiki Medical/Clinical • Wiki Proteins • Wiki Chemicals • Wiki Etc. • Allow for: • Community Annotation • Quick growth of terminology systems • Semantic Linking between concepts

  20. Association Matrix Meta-analysis Literature Knowlet Expert Challenge Protein A Update WikiZ/P Expert comments U.W. Fingerprint Peer to Peer Review Final Approval

  21. Solid (a) Liquid (b) Gas (C) 0.1 0.9 0.4 New publications or annotations Central Annotation Proposals to Data bases ? Discussion Voting in Wiki Reduction False Positives Meta-analysis Proximity measures 1st order Semantic enrichment

  22. Science Wiki’s • UID from WiktionaryZ • Research information • Talk-page • Liquid Threads • Object Knowlets • REGISTRATION (1X) • Unique Author ID • E-mail Adress • PHP/userpage • People Knowlets Wiki-Authors Wiki-X • UID from WiktionaryZ • Articles about UID’s • Encyclopaedic/ NPOV • Anonymous allowed • Unique concept ID • Language variants • Homonyms • Definitions (brief) • Object Knowlets Omegawiki.org Wikipedia

  23. Nature News February 15, 2007

  24. The Knowlet Visualization v v v v v v v v + v v v v v + v v v + + + ? ? + + ? + + ? v v + + + + v v ? v ? + ? + + ? + v ? ? v v ? ? + + ? + + ? ? v v ? ? ? v + ? + + v ? ? v + + v ? ? ? Step 2: select concepts of interest F+, C+, A+ F-, C+, A + F-, C-, A+ v + ? Semantic distance to: Malaria Core concept: Malaria (mean distance 5) chloroquine v v primaquine v v New Drug ???? v ? Para-amino-benzoic acid v v Cellular Memberan (GO) v Mosquitoes v Plasmodium Chabaudi v

  25. Core concept: Malaria (mean distance 5) chloroquine v v primaquine v v New Drug ???? v ? Para-amino-benzoic acid v v Cellular Memberan (GO) v Mosquitoes v

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