John T Halloran
Title
Cited by
Cited by
Year
Distributed cognitive radio network management via algorithms in probabilistic graphical models
Y Liang, L Lai, J Halloran
IEEE Journal on Selected Areas in Communications 29 (2), 338-348, 2011
212011
Classification: Naive bayes vs logistic regression
J Halloran
Technical report, 2009
182009
Learning peptide-spectrum alignment models for tandem mass spectrometry
JT Halloran, JA Bilmes, WS Noble
Uncertainty in artificial intelligence: proceedings of the... conference …, 2014
132014
Dynamic bayesian network for accurate detection of peptides from tandem mass spectra
JT Halloran, JA Bilmes, WS Noble
Journal of proteome research 15 (8), 2749-2759, 2016
122016
Distributed algorithm for collaborative detection in cognitive radio networks
Y Liang, L Lai, J Halloran
2009 47th annual allerton conference on communication, control, and …, 2009
112009
Spectrum identification using a dynamic bayesian network model of tandem mass spectra
AP Singh, J Halloran, JA Bilmes, K Kirchoff, WS Noble
Uncertainty in artificial intelligence: proceedings of the... conference …, 2012
102012
Gradients of generative models for improved discriminative analysis of tandem mass spectra
JT Halloran, DM Rocke
Advances in neural information processing systems 30, 5724, 2017
82017
Speeding Up Percolator
JT Halloran, H Zhang, K Kara, C Renggli, M The, C Zhang, DM Rocke, ...
Journal of proteome research 18 (9), 3353-3359, 2019
52019
A matter of time: faster percolator analysis via efficient svm learning for large-scale proteomics
JT Halloran, DM Rocke
Journal of proteome research 17 (5), 1978-1982, 2018
52018
Jensen: An easily-extensible c++ toolkit for production-level machine learning and convex optimization
R Iyer, JT Halloran, K Wei
arXiv preprint arXiv:1807.06574, 2018
42018
Learning concave conditional likelihood models for improved analysis of tandem mass spectra
JT Halloran, DM Rocke
Advances in neural information processing systems 31, 5420, 2018
32018
Analyzing tandem mass spectra using the drip toolkit: Training, searching, and post-processing
JT Halloran
Data Mining for Systems Biology, 163-180, 2018
22018
Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies
J Liu, JT Halloran, JA Bilmes, RM Daza, C Lee, EM Mahen, D Prunkard, ...
Scientific reports 7 (1), 1-13, 2017
22017
Deep Semi-Supervised Learning Improves Universal Peptide Identification of Shotgun Proteomics Data
JT Halloran, G Urban, DM Rocke, PF Baldi
bioRxiv, 2020
12020
GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification
JT Halloran, DM Rocke
Advances in Neural Information Processing Systems 33, 2020
12020
Faster and more accurate graphical model identification of tandem mass spectra using trellises
S Wang, JT Halloran, JA Bilmes, WS Noble
Bioinformatics 32 (12), i322-i331, 2016
12016
Training HMMs in GMTK
JT Halloran
University of Washington, Seattle, 2015
12015
GMTK Tutorial on Dynamic Graphical Model Training with Gaussian Mixture Unaries, using TIMIT
R Rogers, JA Bilmes, JT Halloran
University of Washington, Seattle, 2015
12015
Faster graphical model identification of tandem mass spectra using peptide word lattices
S Wang, JT Halloran, JA Bilmes, WS Noble
arXiv preprint arXiv:1410.7875, 2014
12014
Analyzing Tandem Mass Spectra: A Graphical Models Perspective.
JT Halloran
AMBN, 6, 2017
2017
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