Accepted Posters
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Category 'P'- Ontologies' |
| Poster P1 |
| Identifying informative subsets of Gene Ontology terms using Information Bottleneck Methods |
| Bo Jin- Medical University of South Carolina |
| Xinghua Lu (Medical University of South Carolina, Biochemistry and Molecular Biology); |
| Short Abstract: Based on the principled information bottleneck framework, we have developed a novel algorithm for identifying informative subsets of Gene Ontology terms to meet the domain-specific requirements. The algorithm sequentially removes Gene Ontology terms from the graph with the constraint that the loss of semantic information is minimized. |
| Long Abstract:Click Here |
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| Poster P2 |
| Ontology Based Phenotype Database and Mining Tool |
| Mary Shimoyama- Medical College of Wisconsin |
| Liz Worthey (Medical College of Wisconsin, Human and Molecular Genetics Center); Jennifer Smith (Medical College of Wisconsin, Human and Molecular Genetics Center); Rajni NIgam (Medical College of Wisconsin, Human and Molecular Genetics Center); Victoria Petri (Medical College of Wisconsin, Human and Molecular Genetics Center); Stan Laulederkind (Medical College of Wisconsin, Human and Molecular Genetics Center); Tim Lowry (Medical College of Wisconsin, Human and Molecular Genetics Center); Tom Hayman (Medical College of Wisconsin, Human and Molecular Genetics Center); Shur-Jen Wang (Medical College of Wisconsin, Human and Molecular Genetics Center); Jeff dePons (Medical College of Wisconsin, Human and Molecular Genetics Center); Pushkala Jayaraman (Medical College of Wisconsin, Human and Molecular Genetics Center); Marek Tutaj (Medical College of Wisconsin, Human and Molecular Genetics Center); Weisong Liu (Medical College of Wisconsin, Human and Molecular Genetics Center); Diane Munzenmaier (Medical College of Wisconsin, Human and Molecular Genetics Center); Melinda Dwinell (Medical College of Wisconsin, Human and Molecular Genetics Center); Simon Twigger (Medical College of Wisconsin, Human and Molecular Genetics Center); Howard Jacob (Medical College of Wisconsin, Human and Molecular Genetics Center); |
| Short Abstract: RGD uses standard data formats and ontologies to integrate phenotype data from multiple sources.This has facilitated the development of a phenotype data mining tool which allows users to search for phenotype information using a series of filters based on clinical measurement type, strain, measurement method and/or experimental |
| Long Abstract:Click Here |
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| Poster P3 |
| Biological validation of gene expression clusters based on the semantic similarities of Gene Ontology annotations |
| Marcelo Soria- College of Agronomy. University of Buenos Aires |
| Guillermo Henrion (Facultad de Ciencias Exactas y Natualres. Universidad de Buenos Aires, Departamento de Computacion); Ana Haedo (Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires, Departamento de Computacion); Fabiana Bigi (CNIA-INTA Castelar, Instituto de Biotecnologia); |
| Short Abstract: A permutation test for cluster validation based on the semantic similarities of Gene Ontology annotations was used in tandem with the silhouette measure of internal consistency. The double validation procedure was applied to microarray gene expression data from Mycobacterium tuberculosis to obtain gene sets appropriate for upstream conserved motif searches. |
| Long Abstract:Click Here |
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| Poster P4 |
| An Application Ontology for Gene Expression Sample Variables |
| James Malone- European Bioinformatics Institute |
| Tomasz Adamusiak (EBI, Microarray Informatics); Ele Holloway (EBI, Microarray Informatics); Jie Zheng (University of Pennsylvania , Center for Bioinformatics); Misha Kapushesky (EBI, Microarray Informatics); Nikolay Kolesnikov (EBI, Microarray Informatics); Anna Zhukova (EBI, Microarray Informatics); Alvis Brazma (EBI, Microarray Informatics); Helen Parkinson (EBI, Microarray Informatics); |
| Short Abstract: Experimental descriptions submitted to the Gene Expression Atlas at EBI cover many biological domains. We describe the Experimental Factor Ontology (EFO), an application ontology, designed to meet application focused use cases. Our methodology and tools for consuming reference ontologies allow annotation and integration of data and provide advanced querying. |
| Long Abstract:Click Here |
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| Poster P5 |
| OntoCAT - a simpler way to access ontology resources |
| Tomasz Adamusiak- European Bioinformatics Institute |
| K Joeri van der Velde (Genomics Coordination Center, University Medical Center Groningen and Groningen Bioinformatics Center, University of Groningen, Department of Genetics); Niran Abeygunawardena (European Bioinformatics Institute, Microarray Informatics); Despoina Antonakaki (Genomics Coordination Center, University Medical Center Groningen and Groningen Bioinformatics Center, University of Groningen, Department of Genetics); Helen Parkinson (European Bioinformatics Institute, Microarray Informatics); Morris A. Swertz (Genomics Coordination Center, University Medical Center Groningen and Groningen Bioinformatics Center, University of Groningen, Department of Genetics); |
| Short Abstract: OntoCAT or Ontology Common API Tasks is an open source package developed to simplify the task of querying heterogeneous ontology resources. It supports NCBO BioPortal and EBI Ontology Lookup Service (OLS), as well as local OWL and OBO fi les. It is available from http://ontocat.sourceforge.net |
| Long Abstract:Click Here |
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| Poster P6 |
| GeneRIFcompendiate: Quantitative gene annotation using collective GeneRIF associations and ontology terms |
| Pan Du- Northwestern University |
| Simon Lin (Northwestern University, The Biomedical Informatics Center); Gang Feng (Northwestern University, The Biomedical Informatics Center); Warren Kibbe (Northwestern University, The Biomedical Informatics Center); |
| Short Abstract: We propose a systemized, dynamic method for generating an annotation compendium coupling literature and disease ontology terms using GeneRIFs. Quantitative scores are estimated by testing the enrichment of ontology terms associated with a gene. A miniSet is then defined to summarize the major functions associated with these ontology terms. |
| Long Abstract:Click Here |
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| Poster P7 |
| Biomedical ontologies for Parasite research |
| Vinh Nguyen- Knoesis center, Wright State University |
| Satya Sahoo (Kno.e.sis center, Wright State Univesrity, Department of Computer Science and Engineering); Priti Parikh (Kno.e.sis center, Wright State Univesrity, Department of Computer Science and Engineering); Amit Sheth (Kno.e.sis center, Wright State Univesrity, Department of Computer Science and Engineering); Todd Minning (Tarleton Research Group, University of Georgia, Department of Cellular Biology); Brent Weatherly (Tarleton Research Group, University of Georgia, Department of Cellular Biology); Rick Tarleton (Tarleton Research Group, University of Georgia, Department of Cellular Biology); Flora Logan (Sanger Institute, ); |
| Short Abstract: In this work, we identify the common problem in querying data over heterogeneous formats in biomedical domain. We describe ontological approach to integrate multiple data sources and provide a visual mechanism to query over those data sets, primarily using two domain-specific Parasite Lifecycle and Parasite Experiment ontologies. |
| Long Abstract:Click Here |
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Accepted Posters
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