Preprints
  1. John T. Halloran, Gregor Urban, David Rocke, and Pierre Baldi.
    Deep Semi-Supervised Learning Improves Universal Peptide Identification of Shotgun Proteomics Data.
    bioRxiv, 2021.
    [Paper], [Software]
  2. Rishabh Iyer, John T. Halloran, and Kai Wei.
    Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization.
    Arxiv, abs/1807.06574, 2018.
    [Paper], [Software]
Publications
Peer-reviewed Conference and Workshop Papers
  1. John T. Halloran and David M. Rocke.
    GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification.
    Advances in Neural Information Processing Systems (NeurIPS). 2020
    20% Acceptance rate, 1900 out of 9454 submissions.
    [Paper], [Poster] [Code]
  2. John T. Halloran and David M. Rocke
    Fast Massive-Scale SVM Learning for Proteomics Analysis.
    ICML Workshop on Computational Biology (WCB@ICML 2020). 2020
  3. John T. Halloran and David M. Rocke
    GPU-Accelerated SVM Learning for Large-Scale Proteomics Analysis.
    Machine Learning in Computational Biology (MLCB) Meeting. 2019
  4. John T. Halloran and David M. Rocke.
    Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra.
    Advances in Neural Information Processing Systems (NeurIPS). 2018
    20.8% Acceptance rate, 1011 out of 4856 submissions.
    [Code], [PDF]
  5. John T. Halloran and David M. Rocke.
    Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra.
    Advances in Neural Information Processing Systems (NIPS). 2017
    Spotlight presentation; 3.5% Acceptance rate, 112 out of 3240 submissions.
    [PDF], [Supplementary]
  6. Shengjie Wang, John T. Halloran, Jeff A. Bilmes and William S. Noble.
    Faster and more accurate graphical model identification of tandem mass spectra using trellises.
    Conference on Intelligent Systems for Molecular Biology (ISMB). 2016
    [PDF]
  7. John T. Halloran and David M. Rocke,
    Fisher Kernels for Improved Analysis of Tandem Mass Spectra,
    Neural Information Processing Systems (NIPS) Workshop on Machine Learning in Computational Biology (MLCB). 2016
  8. John T. Halloran, Jeff A. Bilmes, and William S. Noble.
    Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry,
    Uncertainty in Artificial Intelligence (UAI). 2014
    [PDF], [Supplementary Data]
  9. Ajit P. Singh, John Halloran, Jeff A. Bilmes, Katrin Kirchoff, William S. Noble,
    Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra,
    Uncertainty in Artificial Intelligence (UAI). 2012
    [PDF]
  10. John T. Halloran, Ajit P. Singh, Jeff A. Bilmes, William S. Noble,
    Peptide Identification of Tandem Mass Spectra via Spectrum Alignment using a Dynamic Bayesian Network,
    Neural Information Processing Systems (NIPS) Workshop on Machine Learning in Computational Biology (MLCB). 2012
    Extended Abstract and Oral Presentation.
  11. Ajit P. Singh, John Halloran, Jeff A. Bilmes, Katrin Kirchoff, and William S. Noble.
    Spectrum Identification with a Dynamic Bayesian Network model of Tandem Mass Spectra,
    RECOMB Workshops, Satellite Conference on Computational Proteomics. 2012
  12. Yingbin Liang, Lifeng Lai, and John Halloran
    Distributed algorithm for collaborative detection in cognitive radio networks,
    Proc. Allerton Conf. on Communication, Control, and Computing. 2009
    [URL]
Journal Publications
  1. John T. Halloran, Hantian Zhang, Kaan Kara, Cedric Renggli, Matthew The, Ce Zhang, David M. Rocke, Lukas Kall, William Stafford Noble.
    Speeding up Percolator.
    Journal of Proteome Research (JPR). 2019
    [PDF], [Software]
  2. John T. Halloran and David M. Rocke.
    A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics.
    Journal of Proteome Research (JPR). 2018
    [PDF], [Software], [Datasets]
  3. Jie Liu, John T. Halloran, Jeffrey Bilmes, Riza Daza, Choli Lee, Elisabeth Mahen, Donna Prunkard, Chaozhong Song, Sibel Blau, Michael Dorschner, Vijayakrishna Gadi, Jay Shendure, Anthony Blau, and William Noble.
    Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies.
    Scientific Reports. 2017
    [PDF], [URL] [Software]
  4. John T. Halloran, Jeff A. Bilmes, and William, S. Noble.
    A dynamic Bayesian network for accurate detection of peptides from tandem mass spectra.
    Journal of Proteome Research (JPR). 2016
    [PDF], [Software]
  5. Yingbin Liang, Lifeng Lai, and John Halloran.
    Distributed cognitive radio network management via algorithms in probabilistic graphical methods.
    IEEE JSAC, Special Issue on Advances in Cognitive Radio Networking and Communications, Feb. 2011.
    [PDF]
Book Chapters
  1. John T. Halloran.
    Analyzing Tandem Mass Spectra using the DRIP Toolkit: Training, Searching, and Post-Processing.
    Data Mining for Systems Biology: Methods and Protocols, Ed. by H. Mamitsuka. 2018
    [Book Chapter]
Theses
  1. John T. Halloran
    Graphical Models for Peptide Identification of Tandem Mass Spectra.
    Ph.D. Dissertation, University of Washington, Department of Electrical Engineering, 2016.
    [PDF]
  2. John Halloran,
    Probabilistic Graphical Models and Random Graphs with Applications to Wireless Communications and Data Compression.
    Master's Thesis, UH Manoa, Department of Electrical Engineering, 2010.