Florian Markowetz

Summary

Affiliation: Princeton University
Country: USA

Publications

  1. ncbi Inferring cellular networks--a review
    Florian Markowetz
    Max Planck Institute for Molecular Genetics, Ihnestrasse 63 73, 14195 Berlin, Germany
    BMC Bioinformatics 8:S5. 2007
  2. ncbi Non-transcriptional pathway features reconstructed from secondary effects of RNA interference
    Florian Markowetz
    Department of Computational Molecular Biology, Computational Diagnostics Group, Max Planck Institute for Molecular Genetics Ihnestrasse 63 73, 14195 Berlin, Germany
    Bioinformatics 21:4026-32. 2005
  3. ncbi Deregulation upon DNA damage revealed by joint analysis of context-specific perturbation data
    Ewa Szczurek
    Computational Molecular Biology Department, Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany
    BMC Bioinformatics 12:249. 2011
  4. ncbi Nested effects models for high-dimensional phenotyping screens
    Florian Markowetz
    Lewis Sigler Institute for Integrative Genomics and Department of Computer Science, Princeton University, Princeton, NJ, 08544, USA
    Bioinformatics 23:i305-12. 2007
  5. ncbi Structure learning in Nested Effects Models
    Achim Tresch
    Johannes Gutenberg University Mainz
    Stat Appl Genet Mol Biol 7:Article9. 2008
  6. ncbi Computational identification of cellular networks and pathways
    Florian Markowetz
    Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
    Mol Biosyst 3:478-82. 2007
  7. ncbi Computational diagnostics with gene expression profiles
    Claudio Lottaz
    Max Planck Institute for Molecular Genetics and Berlin Center for Genome Based Bioinformatics, Berlin, Germany
    Methods Mol Biol 453:281-96. 2008

Detail Information

Publications7

  1. ncbi Inferring cellular networks--a review
    Florian Markowetz
    Max Planck Institute for Molecular Genetics, Ihnestrasse 63 73, 14195 Berlin, Germany
    BMC Bioinformatics 8:S5. 2007
    ..The second part discusses probabilistic and graph-based methods for data from experimental interventions and perturbations...
  2. ncbi Non-transcriptional pathway features reconstructed from secondary effects of RNA interference
    Florian Markowetz
    Department of Computational Molecular Biology, Computational Diagnostics Group, Max Planck Institute for Molecular Genetics Ihnestrasse 63 73, 14195 Berlin, Germany
    Bioinformatics 21:4026-32. 2005
    ....
  3. ncbi Deregulation upon DNA damage revealed by joint analysis of context-specific perturbation data
    Ewa Szczurek
    Computational Molecular Biology Department, Max Planck Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany
    BMC Bioinformatics 12:249. 2011
    ..Accumulating knowledge about biological networks creates an opportunity to study these changes in their cellular context...
  4. ncbi Nested effects models for high-dimensional phenotyping screens
    Florian Markowetz
    Lewis Sigler Institute for Integrative Genomics and Department of Computer Science, Princeton University, Princeton, NJ, 08544, USA
    Bioinformatics 23:i305-12. 2007
    ....
  5. ncbi Structure learning in Nested Effects Models
    Achim Tresch
    Johannes Gutenberg University Mainz
    Stat Appl Genet Mol Biol 7:Article9. 2008
    ..Third, we show that the new formulation of the likelihood allows efficiency in traversing model space. Fourth, we incorporate prior knowledge and an automated variable selection criterion to decrease the influence of noise in the data...
  6. ncbi Computational identification of cellular networks and pathways
    Florian Markowetz
    Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
    Mol Biosyst 3:478-82. 2007
    ....
  7. ncbi Computational diagnostics with gene expression profiles
    Claudio Lottaz
    Max Planck Institute for Molecular Genetics and Berlin Center for Genome Based Bioinformatics, Berlin, Germany
    Methods Mol Biol 453:281-96. 2008
    ..In this process, they encounter a series of obstacles and pitfalls. This chapter reviews fundamental issues from machine learning and recommends a procedure for the computational aspects of a clinical micro-array study...