SEMS: Model search and ranked Retrieval (Ron Henkel)
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SEMS: Model search and ranked Retrieval (Ron Henkel)

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Ron Henkel's presentation of our Ranked Retrieval approach; 2012 PALs meeting of the Sysmo-SEEK project in Heidelberg, Germany. 28th-30th of November 2012.

Ron Henkel's presentation of our Ranked Retrieval approach; 2012 PALs meeting of the Sysmo-SEEK project in Heidelberg, Germany. 28th-30th of November 2012.

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    SEMS: Model search and ranked Retrieval (Ron Henkel) SEMS: Model search and ranked Retrieval (Ron Henkel) Presentation Transcript

    • Graph based storage and retrieval of computational models Ron Henkel, Martin Scharm, Dagmar Waltemath, Olaf Wolkenhauer Department of Systems Biology and Bioinformatics University of Rostock www.sbi.uni-rostock.de29.11.2012 © 2009 UNIVERSITÄT ROSTOCK
    • Motivation 1000 120000 900 100000 800 700 80000 Number of AnnotationsNumber of Models 600 500 60000 400 40000 300 200 20000 100 0 0 Models Apr Jul Okt Jan Apr Jul Okt Jan Apr Jul Okt Jan Apr Jul Okt Jan Apr Jul Okt Jan Apr Jul Okt Jan Apr Jul Okt Jan Apr Jul Annotation 05 05 05 06 06 06 06 07 07 07 07 08 08 08 08 09 09 09 09 10 10 10 10 11 11 11 11 12 12 12 Data from BioModels Database 11.12.2012 © 2009 UNIVERSITÄT ROSTOCK 2
    • Motivation • Models:  Grow in number and complexity  Are provided with supplementary material  Evolve over time11.12.2012 © 2009 UNIVERSITÄT ROSTOCK 3
    • State of the Art • Storage:  Relational Databases  Model files on Hard Disk Drive (HDD)  Additional files (images, result sets, paper) • Search:  SQL statements  Facetted search  Data browsing29.11.2012 © 2009 UNIVERSITÄT ROSTOCK 4
    • State of the Art - Demo11.12.2012 © 2009 UNIVERSITÄT ROSTOCK 5
    • Available Data for Ranked Retrieval Model file Annotation & Ontologies A model‘s network• Constituent names • Biochemical background • Model structure• Model code • Synonyms • Aggregate values 29.01.2012 © 2009 UNIVERSITÄT ROSTOCK 6
    • Available Data for Ranked Retrieval# aspect importance contained features1 Administrative none ids, file name, version, formalism…2 Person medium creator, encoder, submitter, publication author3 Dates low creation and modification date4 Publication high title, abstract, full-text, journal5 Constituents very high compartment, species, reaction6 User content very high keywords, tags, remarks, changes • The concept is abstract and can be applied to different model formalisms. • Depending on the formalism the aspects can be refined into features. • The model constituents also contain the annotations. Henkel et al. (2010) BMC Bioinf11.12.2012 © 2009 UNIVERSITÄT ROSTOCK 7
    • Biomodels Database – A Test Case • Apache Lucene Framework • Model Index  425 models, 140.977 terms • Semantic Index  2261 URIs, 409.124 terms http://www.ebi.ac.uk/biomodels-demo/11.12.2012 © 2009 UNIVERSITÄT ROSTOCK 8
    • Demo11.12.2012 © 2009 UNIVERSITÄT ROSTOCK 9
    • Improvements • Ranking • Enhanced query possibilities  Required, optional and excluded criteria  Allow full-text and Ontology queries • Example: “Find cell cycle models” Query BiomodelsDB Using IR Gold Standard cell cycle 135 173 n/a “cell cycle” 14 26 2811.12.2012 © 2009 UNIVERSITÄT ROSTOCK 10
    • Available Data for Ranked Retrieval Model based Annotation & Ontologies A model‘s network• Model name • Biochemical background • Include model structure• Model code • Allows to identify e.g. synonyms • Aggregate values 29.11,2012 © 2009 UNIVERSITÄT ROSTOCK 11
    • Mapping a Model to a Database A model‘s network• Include model structure• Aggregate values 29.11.2012 © 2009 UNIVERSITÄT ROSTOCK 12
    • Advantages of Graph Databases • Easy mapping of model structure • Fast browsing through models • Flexible and schema-free storage • Easy linking to models, simulation setups or results, and external resources11.12.2012 © 2009 UNIVERSITÄT ROSTOCK 13
    • Document Model R P S E C asProduct asReactant asModifier isEncodedBy isVersionOf is is is uniprot:P0710 uniprot:Q0339 SBO:0000268 HGNC:8582 GO:0005737 1 311.12.2012 © 2009 UNIVERSITÄT ROSTOCK 14
    • 11.12.2012 © 2009 UNIVERSITÄT ROSTOCK 15
    • Preliminary Results • All models stored in Biomodels DB were stored into the graph database • Implemented storage and search in Jummp  official demo release upcoming • Added 140.811 models from path2models project  done, but including annotation blows the memory  database scales well and is reasonably fast11.12.2012 © 2009 UNIVERSITÄT ROSTOCK 16
    • Demo11.12.2012 © 2009 UNIVERSITÄT ROSTOCK 17
    • Future Work: Relate model versions • Link successor and predecessor • Relate changed entities • Store the diff • Enable version control for multi- document models • Propagate changes for imported models11.12.2012 © 2009 UNIVERSITÄT ROSTOCK 18
    • SEMS: Methods for Model & Simulation Management Model Version control Model Storage Model Search• XML version control • Relational databases • Ranked model retrieval Waltemath et al., 2011 (DBSpektrum) Henkel et al., 2010 (BMC Bioinf)• Difference detection in XML Waltemath et al., submitted • Graph-based storage • Structure- and Henkel et al., 2012 (INFORMATIK) ontology-based search Simulation VC Simulation Storage SimulationSearch• Standardized encoding of simulation setups Waltemath et al., 2011 (BMC SysBiol)• Linking models and simulation descriptions Henkel et al., 2012 (INFORMATIK) 11.12.2012 © 2009 UNIVERSITÄT ROSTOCK 19
    • Take Home Message • Ranked retrieval is a necessary feature for model databases. • The model’s inherent structure should be queryable. • Graph based storage reflects well a model‘s encoding and evolution.11.12.2012 © 2009 UNIVERSITÄT ROSTOCK 20
    • Thanks for your attention. Questions? ron.henkel@uni-rostock.de11.12.2012 © 2009 UNIVERSITÄT ROSTOCK 21