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

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 Kalupson • 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. Why ? The Million Minds Approach

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

6. Too much to read: major trends foreseen: • From Reading to Consulting • From Reading to Meta Analysis • From Texts to Facts • To Central AND COMMUNITY Annotation

7. 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.

8. 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

9. Ambiguity 1: Synonyms Relatively straightforward: Thesaurus • Facilitating networks of information. van Mulligen EM, Diwersy M, Schmidt M, Buurman H, Mons B • Proceedings of AMIA Symposium 2000, 868-72

10. Ambiguity 2: Homonyms PSA Prostate Specific Antigen PSoriatic Arthritis alpha-2,8-PolySialic Acid PolySubstance Abuse Picryl Sulfonic Acid Polymeric Silicic Acid Partial Sensory Agnosia Poultry Science Association Not so easy: context needed • Distribution of information in biomedical abstracts and full-text publications, Schuemie MJ, Weeber M, Schijvenaars BJ, van Mulligen EM, • van der Eijk CC, Jelier R, Mons B, Kors JA, Bioinformatics 2004Nov 1, 20:2597-604

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

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

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

14. 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

15. 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)

16. 1 Million person organisation Object 1 Object 2 Object 3 drug disease 3. Building an association matrix of large data sources 1 Million gene

17. 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)

18. 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

19. 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

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

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

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