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Dr. Nico Pfeifer

Dr. Nico Pfeifer

Max-Planck-Institut für Informatik
Statistical Learning in Computational Biology

Campus E1 4
66123 Saarbrücken, Germany
email:   npfeifer@mpi-inf.mpg.de
phone:   +49 681 9325 3019
fax:   +49 681 9325 3099
room:   519 (Building E1 4)


Publications

Conference Talks

  • N. Pfeifer
    “Junior Research Group Presentation” of my group “Statistical Learning in Computational Biology”
    German Conference on Bioinformatics 2015
  • A. Feldmann and N. Pfeifer
    From Predicting to Analyzing HIV-1 Resistance to Broadly Neutralizing Antibodies
    German Conference on Bioinformatics 2015
  • N. K. Speicher and N. Pfeifer
    Integrating Different Data Types by Regularized Unsupervised Multiple Kernel Learning with Application to Cancer Subtype Discovery
    ISMB/ECCB 2015
  • A. Jalali and N. Pfeifer
    Interpretable per Case Weighted Ensemble Method for Cancer Associations
    Workshop on Algorithms in Bioinformatics (WABI) 2014
  • N. Pfeifer and T. Lengauer
    Improving HIV coreceptor usage prediction in the clinic using hints from next generation sequencing data
    11th European Conference on Computational Biology (ECCB 2012)
  • N. Pfeifer and T. Lengauer
    New methods and new data to improve HIV coreceptor usage prediction
    Arevir-GenaFor-Meeting 2012
  • N. Pfeifer and O. Kohlbacher:
    Predicting Binding Affinities of MHC Class II Epitopes Across Alleles
    NIPS Machine Learning in Computational Biology workshop, Whistler 2008
  • N. Pfeifer and O. Kohlbacher:
    Multiple Instance Learning Allows MHC Class II Epitope Predictions across Alleles
    8th Workshop on Algorithms in Bioinformatics (WABI 2008)
  • N. Pfeifer, C. G. Huber, A. Leinenbach, O. Kohlbacher
    Improving Identification with new Machine Learning Techniques. Dagstuhl Seminar Computational Proteomics, Dagstuhl 2008
  • N. Pfeifer, C. G. Huber, A. Leinenbach, O. Kohlbacher:
    Statistical learning of peptide retention behavior in chromatographic separations: A new kernel-based approach for computational proteomics.
    NIPS Machine Learning in Computational Biology workshop, Whistler 2007
  • O. Kohlbacher, K. Reinert, C. Gröpl, E. Lange, N. Pfeifer, O. Schulz-Trieglaff, M. Sturm
    TOPP - The OpenMS Proteomics Pipeline
    ECCB 2006

Technical Reports