Prof. Dr. rer. nat. Ulf Leser

Head of the Knowledge Management in Bioinformatics,

HU Berlin

 

http://www2.informatik.hu-berlin.de/~leser/

Brief CV & Research focus

University Education

  • 1995 Diploma in Computer Science, TU München
  • 2000 Dissertation, TU Berlin (with distinction)

Professional Experience (selection)

  • 1995-1997 Database developer, MPI-MG
  • 1997-2000 Graduate School for Distributed Information Systems
  • 2000-2003 Project Manager, PSI AG
  • 2003- Professor, HU Berlin

Research Fields

  • Biomedical knowledge management
  • Scientific workflows
  • Analysis of –omics data

Scientific Scope and Goals

The group on "Knowledge Management in Bioinformatics" has a proven record of successful research and applications especially in biomedical databases, scientific workflows, statistical analysis of high-throughput experiments, and biomedical text mining. It gathers more than 15 years experience in managing and analyzing biomedical data set, especially from high-throughput experiments. Previous and current projects in this area are concerned with analysis and management of proteins and their function, phenotype data, cancer-related knowledge bases, protein-protein-interaction networks, and sequence / microarray / ChIP-Chip / ChIP-seq data sets. The group has developed several popular integrated biomedical databases (e.g. IXDB, COLUMBA) and biomedical search engines (GeneView, Alibaba). The group currently consists of 12 scientists, all of which work on topics related to biomedical data and biomedical knowledge management. Prof. Leser currently is part of six publicly funded projects in biomedical research and a member of two publicly funded Graduate Schools and one research unit.

Most important Awards, Grants or Scientific Achievements

  • PI in several Medical Systems Biology projects (ColoNet, OncoPath, T-SYS, Prositu, …)
  • PI in research unit Stratosphere

10 most relevant Publications

  • Groth, P., Kalev, I., Kirov, I., Traikov, B., Leser, U. and Weiss, B. (2010). "Phenoclustering: Online mining of cross-species phenotypes." Bioinformatics 26(15): 1924-1925.
  • Thomas, P., Starlinger, J., Vowinkel, A., Arzt, S. and Leser, U. (2012). "GeneView: A comprehensive semantic search engine for PubMed." Nucleic Acids Res 40(Web Server issue): 585-591.
  • Tikk, D., Thomas, P., Palaga, P., Hakenberg, J. and Leser, U. (2010). "A comprehensive benchmark of kernel methods to extract protein-protein interactions from literature." PLOS Computational Biology 6(7).
  • Jaeger, S., Ertaylan, G., van Dijk, D., Leser, U. and Sloot, P. M. (2010). "Inference of Surface Membrane Factors of HIV-1 Infection through Functional Interaction Networks." PLoS One 5:10.
  • Hakenberg, J., Plake, C., Royer, L., Strobelt, H., Leser, U. and Schroeder, M. (2008). "Gene mention normalization and interaction extraction with context models and sentence motifs." Genome Biol 9 Suppl 2: S14.
  • Joosten, M., Seitz, V., Zimmermann, K., Sommerfeld, A., Berg, E., Lenze, D., Leser, U., Stein, H. and Hummel, M. (2012). "Histone acetylation and DNA demethylation of T-cells result in an anaplastic large cell lymphoma-like phenotype." Haematologica (accepted).
  • Jaeger, S., Sers, C. and Leser, U. (2010). "Combining modularity, conservation, and interactions of proteins significantly increases precision and coverage of protein function prediction." BMC Genomics 11(717).
  • Seitz, V., Thomas, P., Zimmermann, K., Paul, U., Ehlers, A., Joosten, M., Dimitrova, L., Lenze, D., Sommerfeld, A., Oker, E., et al. (2011). "Classical Hodgkin lymphoma shows epigenetic features of an abortive plasma cellular differentiation." Haematologica 96(6): 863-870.
  • Koschmieder, A., Zimmermann, K., Trißl, S. and Leser, U. (2012). "Tools for Managing and Analyzing Microarray Data." Briefings in Bioinformatics 13: 46-60.
  • Rocktäschel, T., Weidlich, M. and Leser, U. (2012). "ChemSpot: A Hybrid System for Chemical Named Entity Recognition." Bioinformatics 28(12): 1633-1640.

Perspectives for Future PhD Thesis Projects

We seek enthustic and skillful PhD candidates with a solid background in cancer biology and in bioinformatics / systems biology for a project on in-silico reconstructing the human core regulatory network, using a mixture of experimental and literature-derived data. Applications hould bring at least basic knowledge in statistical data analysis and machine learning. The project will be co-hosted by Prof. Nils Blüthgen, Medical Systems Biology, Charite Berlin.