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

Publications of Author

  1. Susan L. Havre, Bobbie-Jo M. Webb-Robertson, Anuj R. Shah, Christian Posse, Banu Gopalan, Fred J. Brockman
    Bioinformatic Insights from Metagenomics through Visualization. [Citation Graph (0, 0)][DBLP]
    CSB, 2005, pp:341-350 [Conf]
  2. Pak Chung Wong, Elizabeth G. Hetzler, Christian Posse, Mark A. Whiting, Susan Havre, Nick Cramer, Anuj R. Shah, Mudita Singhal, Alan Turner, Jim Thomas
    IN-SPIRE InfoVis 2004 Contest Entry. [Citation Graph (0, 0)][DBLP]
    INFOVIS, 2004, pp:- [Conf]
  3. Anuj R. Shah, Karl J. Castleton, Bonnie L. Hoopes
    Framework for Risk Analysis in Multimedia Environmental Systems: Modeling Individual Steps of a Risk Analysis Process. [Citation Graph (0, 0)][DBLP]
    MSV/AMCS, 2004, pp:38-44 [Conf]
  4. 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]
  5. Anuj R. Shah, Mudita Singhal, Kyle R. Klicker, Eric G. Stephan, H. Steven Wiley, Katrina M. Waters
    Enabling high-throughput data management for systems biology: The Bioinformatics Resource Manager. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2007, v:23, n:7, pp:906-909 [Journal]
  6. 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]

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


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


  9. Machine learning based prediction for peptide drift times in ion mobility spectrometry. [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. A feature vector integration approach for a generalized support vector machine pairwise homology algorithm. [Citation Graph (, )][DBLP]


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