I completed my PhD in the field of computational proteomics in the Biodata Mining & Applied Neuroinformatics Group at the Faculty of Technology at Bielefeld University (Germany) , funded by the International NRW Graduate School of Bioinformatics and Genome Research. Currently, I work with Hanno Steen's group to offer computational support and software development for the analysis of proteomics data. Specifically, I am interested in phosphoproteomics analysis and visualization.

• Application of machine learning / pattern recognition methods in biochemistry
• Visualization of high-dimensional data
• Statistical analysis of biological data

Publications

• Wiebke Timm, Alexandra Scherbart, Sebastian Böcker, Oliver Kohlbacher, and Tim W. Nattkemper. Peak intensity prediction for MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics. BMC Bioinformatics, 9:443, 2008.
  PDF from BMC Bioinformatics
• Wiebke Timm. Peak intensity prediction in mass spectra using machine earning methods. PhD thesis, Intl. Graduate School in Bioinformatics and Genome Research, Bielefeld University, Germany, Sep 2008.
  PDF from BISON
• Alexandra Scherbart, Wiebke Timm, Sebastian Böcker, and Tim Wilhelm Nattkemper. Improved mass spectrometry peak intensity prediction by adaptive feature weighting. In Proceedings of the International Conference on Neural Information Processing (ICONIP), 2008.
  
• Alexandra Scherbart, Wiebke Timm, Sebastian Böcker, and Tim W Nattkemper. Neural network approach for mass spectrometry prediction by peptide prototyping. In Artificial Neural Networks - ICANN 2007, 4669 , 90-99. Springer, 2007.
  PDF
• Alexandra Scherbart, Wiebke Timm, Sebastian Böcker, and Tim W Nattkemper. Som-based peptide prototyping for mass spectrometry peak intensity prediction. In Proceedings of Workshop on Self-Organizing Maps (WSOM'07), 2007.
• Wiebke Timm, Sebastian Böcker, and Tim W Nattkemper. Peak intensity prediction for PMF mass spectra using support vector regression. In Applied Artificial Intelligence - Proceedings of the 7th International FLINS Conference, 565-572. World Scientific, Aug 2006.
  PDF
• Harmen grosse Deters, Wiebke Timm, and Tim Wilhelm Nattkemper. REEF-SOM - a metaphoric data display for exploratory data mining. Brains, Minds and Media, 2, Apr 2006.
  PDF from Bains, Minds & Media
• Tim Wilhelm Nattkemper, Bert Arnrich, Oliver Lichte, Wiebke Timm, Andreas Degenhard, Linda Pointon, Carmel Hayes, Martin O Leach, and UK MARIBS Screening Study. Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods. Artificial Intelligence in Medicine, 32(2), 129-139, 2005.