Prof. Dr. Felix Naumann

Project Description

The huge amount of biological data potentially available for research is a great opportunity to apply existing machine learning algorithms as well as newly developed ones in medical research. To this end, machine learning and data mining methods need to be adapted to the particular needs of bioinformatics. Together with biological data also the publication of scientific results in writing has increased. We therefor apply text mining methods to make these results accessible and easier retrievable.


  • Text Mining in the Medical Domain
  • Data-Intensive Computational Biology

Project-Related Publications

  • 1.
    Heller, D., Krestel, R., Ohler, U., Vingron, M., Marsico, A.: ssHMM: Extracting Intuitive Sequence-Structure Motifs from High-Throughput RNA-Binding Protein Data. Nucleic Acid Research. 45, 11004–11018 (2017).
  • 2.
    Park, J., Blume-Kohout, M., Krestel, R., Nalisnick, E., Smyth, P.: Analyzing NIH Funding Patterns over Time with Statistical Text Analysis. Scholarly Big Data: AI Perspectives, Challenges, and Ideas (SBD 2016) Workshop at AAAI 2016. AAAI (2016).
  • 3.
    Grundke, M., Jasper, J., Perchyk, M., Sachse, J.P., Krestel, R., Neves, M.: TextAI: Enhancing TextAE with Intelligent Annotation Support. Proceedings of the 7th International Symposium on Semantic Mining in Biomedicine (SMBM 2016). pp. 80–84. CEUR-WS.org (2016).