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Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Wissensmanagement in der Bioinformatik

BCB Seminar Mai 2007

Humboldt-Kabinett, Rudower Chaussee 25.


  • 16.00 - 16.45 Uhr: Holger Dach, Fraunhofer Institut SCAI, Sankt Augustin
    @neuLink – Linking Genetics to Disease: A Status Report

    Scientific publications are still the most widely used information resource for getting new research results in the biomedical field. The literature database MEDLINE contains abstracts of almost all published biomedical publications and supports text retrieval by annotations based on the Medical subject headings (MeSH). To enable scientists further to select the most relevant information according to the researchers needs we applied text mining strategies for additional annotations of biomedical entities (e.g. gene, disease, drug).
    Within the @neurIST project (Integrated Biomedical Informatics for the Management of Cerebral Aneurysms) a European initiative in the Sixth Framework Programme Priority 2 of the Information Society Technologies IST we aim at developing an information system called @neuLink, for finding evidences of links between genomics and cerebral aneurysms. In this talk we present a first prototype of a module within the information system, called “Find Significant Genes”, and our approach for large scale literature mining. Moreover, we present a workflow to maintain a continuous entity recognition process which updates the disease-specific knowledge base.

  • 16.45 - 17.00 Uhr: Pause

  • 17.00 - 17.30 Uhr: Philipp Groth, Bayer Schering AG und Humboldt-Universität
    Predicting Protein Function from Phenotypes

    In our study presented in this talk, phenotypes have been used to generate new ways of clustering genes into functional groups. We have used phenotypes – described in textual form – to predict protein function with text clustering methods. I will show that these clusters correlate well with other indicators for functional coherence in gene groups, such as functional annotations from the Gene Ontology (GO) and protein-protein interactions. We have predicted GO-terms from the biological process sub-ontology for some groups with up to 86.5% precision and 64.2% recall. I will discuss some examples for gene groups derived from clustered phenotypes that reveal high biological coherence, and other groups with inconsistent GO-annotations that could be resolved. I will show that systematically analyzing phenotype data on a large scale are well worth the effort.

  • 17.30 - 18.00 Uhr: Dr. Stephan Heymann, Humboldt-Universität
    Positionale Verwaltung von DNA-Sequenzfragmenten zur Genotypanalyse