| Poster P2 |
| The Neuro-Immune Gene Ontology: a subset of GO directed for neurological and immunological systems |
| Nophar Geifman- The Shraga Segal Dept. of Microbiology and Immunology AND The National Institute of Biotechnology in the Negev, Ben Gurion University |
| Alon Monsonego (Ben Gurion University , The Shraga Segal Dept. of Microbiology and Immunology AND The National Institute of Biotechnology in the Negev); Eitan Rubin (Ben Gurion University, The Shraga Segal Dept. of Microbiology and Immunology AND The National Institute of Biotechnology in the Negev); |
| Short Abstract: We propose a new approach for editing the gene ontology, which we call clipping, in which GO is edited according to biological relevance to specific domains. We demonstrate this approach by creating a Neuro-Immune Gene Ontology (NIGO) directed for neurological and immunological systems. |
| Long Abstract: Click Here |
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| Poster P3 |
| Informatics obstacles for integrating evolutionary phenotype diversity with model organism data |
| Hilmar Lapp- National Evolutionary Synthesis Center (NESCent) |
| James Balhoff (US National Evolutionary Synthesis Center, ); Cartik Kothari (US National Evolutionary Synthesis Center, ); Todd Vision (University of North Carolina, Biology); Wasila Dahdul (University of South Dakota, Biology); Paula Mabee (University of South Dakota, Biology); John Lundberg (Academy of Natural Sciences, ); Peter Midford (University of Kansas, Biology); Monte Westerfield (University of Oregon, Biology); |
| Short Abstract: We illustrate the tools, databases, user-interfaces, and semantic data processing we developed to transform the traditionally free-text comparative morphological character descriptions to the same ontology-based formal phenotype assertions employed by the model organism community. The resulting knowledge base integrates across mutant phenotype data for model organisms and evolutionary phenotype diversity. |
| Long Abstract: Click Here |
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| Poster P4 |
| BioPortal: Ontologies and Integrated Data Resources at the Click of a Mouse |
| Patricia Whetzel- Stanford University |
| Natalya F. Noy (Stanford University, Stanford Center for Biomedical Informatics Research); Nigam H. Shah (Stanford University, Stanford Center for Biomedical Informatics Research); Benjamin Dai (Stanford University, Stanford Center for Biomedical Informatics Research); Michael Dorf (Stanford University, Stanford Center for Biomedical Informatics Research); Nicholas Griffith (Stanford University, Stanford Center for Biomedical Informatics Research); Clement Jonquet (Stanford University, Stanford Center for Biomedical Informatics Research); Daniel L. Rubin (Stanford University, Stanford Center for Biomedical Informatics Research); Cherie Youn (Stanford University, Stanford Center for Biomedical Informatics Research); Mark A. Musen (Stanford University, Stanford Center for Biomedical Informatics Research); |
| Short Abstract: BioPortal (http://bioportal.bioontology.org) is an open repository of biomedical ontologies that provides programmatic and web-based access to ontologies developed in OBO, OWL, Protégé frames, and RDF. Features include browsing, searching, and visualization of ontologies. Searching of integrated data resources is also possible through ontology-based indexing of biomedical resources with BioPortal ontologies. |
| Long Abstract: Click Here |
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| Poster P5 |
| Semi-Supervised Clustering Based on the Distance between Gene Pairs in Gene Ontology |
| Dae-Won Kim- Chung-Ang University |
| Song Ko (Chung-Ang University, Computer Science and Engineering); Bo-Yeong Kang (Seoul National University, Dentistry); |
| Short Abstract: Comparative studies using the distance between gene pairs in Gene Ontology for semi-supervised clustering were explored. Here we applied three types of distance measure, Gene Ontology, and data set, respectively, which can provide more practical and general information about utilizing the gene distances for clustering as a prior knowledge. |
| Long Abstract: Click Here |
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| Poster P6 |
| Redundancy Elimination and Visualization of Gene Ontology Term Lists |
| Tomislav Smuc- Rudjer Boskovic Institute |
| Nives Skunca (Rudjer Boskovic Institute, Department of Electronics); Tomislav Smuc (Rudjer Boskovic Institute, Department of Electronics); Fran Supek (Rudjer Boskovic Institute, Department of Electronics); |
| Short Abstract: We propose a clustering-like approach for flexible reduction in size of large user-supplied lists of overlapping Gene Ontology (GO) terms, typically resulting from high-throughput experiments. The remaining GO terms are visualized so as to faithfully reflect the terms' interrelations, relying on measures of semantic similarity in the GO space. |
| Long Abstract: Click Here |
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| Poster P8 |
| Beat: Bio-ontologies enrichment and alignment toolkit |
| Andrea Splendiani- U936 |
| Elena Beisswanger (Jena University, JULIE lab); Olivier Dameron (University of Rennes 1, INSERM U936); John McNaught (University of Manchester, NACTEM); Scott Piao (University of Manchester, NACTEM); Sophia Ananiadou (University of Manchester, NACTEM); Udo Hahn (Jena University, JULIE lab); Anita Burgun (University of Rennes 1, INSERM U936); |
| Short Abstract: BEAT is a toolkit for ontology enrichment and alignment, based on Semantic Web technologies. It allows for modular composition of operations on ontologies. In particular, it supports the alignment of biomedical ontologies offering modules exploiting information from the UMLS Knowledge Sources. |
| Long Abstract: Click Here |
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