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William R. Cannon: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Kenneth D. Jarman, William R. Cannon, Kristin H. Jarman, Alejandro Heredia-Langner
    A Model of Random Sequences for de novo Peptide Sequencing. [Citation Graph (0, 0)][DBLP]
    BIBE, 2003, pp:206-213 [Conf]
  2. Joël M. Malard, Alejandro Heredia-Langner, Douglas J. Baxter, Kristin H. Jarman, William R. Cannon
    Constrained De Novo Peptide Identification via Multi-Objective Optimization. [Citation Graph (0, 0)][DBLP]
    IPDPS, 2004, pp:- [Conf]
  3. Alejandro Heredia-Langner, William R. Cannon, Kenneth D. Jarman, Kristin H. Jarman
    De Novo Analysis of Tandem Mass Spectrometry Data as a Non-Deterministic Optimization Problem. [Citation Graph (0, 0)][DBLP]
    METMBS, 2004, pp:113-117 [Conf]
  4. Bobbie-Jo M. Webb-Robertson, William R. Cannon, Joshua N. Adkins, Deborah K. Gracio
    Session Introduction. [Citation Graph (0, 0)][DBLP]
    Pacific Symposium on Biocomputing, 2006, pp:212-218 [Conf]
  5. 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]
  6. Alejandro Heredia-Langner, William R. Cannon, Kenneth D. Jarman, Kristin H. Jarman
    Sequence optimization as an alternative to de novo analysis of tandem mass spectrometry data. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2004, v:20, n:14, pp:2296-2304 [Journal]
  7. Joël M. Malard, Alejandro Heredia-Langner, William R. Cannon, Ryan W. Mooney, Douglas J. Baxter
    Peptide identification via constrained multi-objective optimization: Pareto-based genetic algorithms. [Citation Graph (0, 0)][DBLP]
    Concurrency and Computation: Practice and Experience, 2005, v:17, n:14, pp:1687-1704 [Journal]

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


  9. SEBINI-CABIN: An Analysis Pipeline for Biological Network Inference, with a Case Study in Protein-Protein Interaction Network Reconstruction. [Citation Graph (, )][DBLP]


  10. A Scalable Parallel Approach for Peptide Identification from Large-Scale Mass Spectrometry Data. [Citation Graph (, )][DBLP]


  11. Session Introduction. [Citation Graph (, )][DBLP]


  12. Current trends in computational inference from mass spectrometry-based proteomics. [Citation Graph (, )][DBLP]


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


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


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