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BIOSEMANTICS Semantic Support Technology For on-line Knowledge Tracking and Discovery Second Order Semantic Enrichment CD40 ligand and tumor necro sis.

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Presentation on theme: "BIOSEMANTICS Semantic Support Technology For on-line Knowledge Tracking and Discovery Second Order Semantic Enrichment CD40 ligand and tumor necro sis."— Presentation transcript:

1 BIOSEMANTICS Semantic Support Technology For on-line Knowledge Tracking and Discovery Second Order Semantic Enrichment 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

2 Rejected Papers Rejected proposals Knowledge generation Hypothesis generation Management of Science (work flow) proposal Peer Review manuscript Project Paper Enriched (annotated) Knowledge Rough Experimental Data = Text-semantic tagging = Semantic matching

3 Too much to read: major trends foreseen: From Reading to Consulting From Reading to Meta Analysis From Writing to Knowledge Representations To Central AND Distributed Annotation

4 textmining Writing = ambiguity Future (hope) Papyrust

5 What do we do ? Disambiguate Text and tag/link concepts –Pre-done for ‘own’ content –On the fly for selected web environments Meta-analyse at concept level Provide meta-analysed information Support Information Based Knowledge Discovery (especially new associations)

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

7 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 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 2004 Nov 1, 20:2597-604

8 But…we have nomenclature committees now…… DEFB4 defensin, beta 4 SAP1, HBD-2, DEFB-2, DEFB102, DEFB2 ELK4 ELK4, ETS-domain protein (SRF accessory protein 1) SAP1PSAP proposin (variant Gaucher disease and variant metachromatic leukodystrophy) SAP1, GLBA PRESENT

9 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 First order semantic enrichment The Knowlet 2 nd order S.E.

10 Second Order Semantic Enrichment 1: Creating Reference Knowlets PSA Prostate Specific Antigen PSA Psoriatic Arthritis Reference Knowlet Reference Knowlet

11 2. Context matching PSA ?? Prostate Specific Antigen Psoriatic Arthritis Reference Knowlet Reference Knowlet New text  93 % correct in  ‘Worst Case Scenario’  98 % overall…. Thesaurus-based disambiguation of gene symbols. Schijvenaars BJ, Mons B, Weeber M, Schuemie MJ, van Mulligen EM, Wain HM, Kors JA BMC Bioinformatics 2005 Jun 16, 6:149 Word sense disambiguation in the biomedical domain: an overview. Schuemie MJ, Kors JA, Mons B, Journal of Computational Biology 2005 Jun, 12:554-65 x

12 Text (free or structured) Resolving ambiguities (contextual reference concepts) Concept Tagging and inserting appropriate links Basic methodology:Concept Tagging, creation and systematic aggregation of Knowlets Text Knowlet Object Knowlets (people, diseases, drugs, genes) Collection Knowlets ( category, pathway, Micro-array-gene-set ) Aggregation of Object KnowletsAggregation of Text Knowlets

13 personorganisationObject 1 gene Object 2 disease Object 3 drug > 15 million Knowlets from PubMed etc. 3. Building an association matrix of large data sources

14 1 0.161 0.300.031 0.280.350.201 0.1880.0040.150.131 A matrix of associative distances meta-analysis Hierarchical Clustering ACS MDS Etc.

15 4. Meta-analysis method 1: ACS Constructing an Associative Concept Space for Literature-based Discovery, van der Eijk CC, van Mulligen EM, Kors JA, Mons B, van den Berg J Journal of the American Society for Information Science and Technology 2004, 55(5): 436-444 Co-occurrence based meta-analysis of scientific texts: retrieving biological relationships between genes. Jelier R, Jenster G, Dorssers LC, van der Eijk CC, van Mulligen EM, Mons B, Kors JA Bioinformatics 2005 May 1, 21:2049-58

16 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 To be Submitted

17 Fingerprints Knowlet Association MatrixMeta-analysis Expert Challenge WikiZ/P Expert comments Peer to Peer Review Final Approval U.W. Fingerprint Update Literature Protein A

18 0.1 0.4 0.9 New publications or annotations Solid Liquid Gas 1 st order Semantic enrichment Reduction False Positives Discussion Voting in Wiki Meta-analysis Proximity measures Proposals to Data bases ? Central Annotation

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

20 Dr. Johan den Dunnen Wiki-Authors OMIM NPOV DMD (Hs) MEI Wiki-Proteins DMD (Hs) AOI

21 Freely provided by Original Source WZ definition (choice) Related concepts (Knowlet) Experts (Wiki-Authors) More about (Wiki-proteins) Vote (Wiki-Proteins) Wikipedia article Browse (Knowlet Browser) Google (e-vamp)

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