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Christopher S. Oehmen: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Christopher S. Oehmen, Lee Ann McCue, Joshua N. Adkins, Katrina M. Waters, Tim Carlson, William R. Cannon, Bobbie-Jo M. Webb-Robertson, Douglas J. Baxter, Elena Peterson, Mudita Singhal, Anuj R. Shah, Kyle R. Klicker
    Analytics challenge - High-throughput visual analytics biological sciences: turning data into knowledge. [Citation Graph (0, 0)][DBLP]
    SC, 2006, pp:299- [Conf]
  2. Christopher S. Oehmen, Douglas J. Baxter, Ryan W. Mooney, Shaun O'Leary, Tim Carlson
    Bandwidth challenge - High throughput feature-matching analysis of biological spectral data. [Citation Graph (0, 0)][DBLP]
    SC, 2006, pp:243- [Conf]
  3. Bobbie-Jo M. Webb-Robertson, Christopher S. Oehmen, Melissa Matzke
    SVM-BALSA: Remote homology detection based on Bayesian sequence alignment. [Citation Graph (0, 0)][DBLP]
    Computational Biology and Chemistry, 2005, v:29, n:6, pp:440-443 [Journal]
  4. Anuj R. Shah, Christopher S. Oehmen, Jill Harper, Bobbie-Jo M. Webb-Robertson
    Integrating subcellular location for improving machine learning models of remote homology detection in eukaryotic organisms. [Citation Graph (0, 0)][DBLP]
    Computational Biology and Chemistry, 2007, v:31, n:2, pp:138-142 [Journal]

  5. Dimension Reduction via Unsupervised Learning Yields Significant Computational Improvements for Support Vector Machine Based Protein Family Classification. [Citation Graph (, )][DBLP]

  6. Support Vector Machine Classification of Probability Models and Peptide Features for Improved Peptide Identification from Shotgun Proteomics. [Citation Graph (, )][DBLP]

  7. PQuad - a visual analysis platform for proteomic data exploration of microbial organisms. [Citation Graph (, )][DBLP]

  8. SVM-HUSTLE - an iterative semi-supervised machine learning approach for pairwise protein remote homology detection. [Citation Graph (, )][DBLP]

  9. A support vector machine model for the prediction of proteotypic peptides for accurate mass and time proteomics. [Citation Graph (, )][DBLP]

  10. A support vector machine model for the prediction of proteotypic peptides for accurate mass and time proteomics. [Citation Graph (, )][DBLP]

  11. Physicochemical property distributions for accurate and rapid pairwise protein homology detection. [Citation Graph (, )][DBLP]

  12. A feature vector integration approach for a generalized support vector machine pairwise homology algorithm. [Citation Graph (, )][DBLP]

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