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John T Halloran

Showing results (1-10 of 9) with videos related to

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Methods in Molecular Biology (Clifton, N.J.)|July 22, 2018
Analyzing Tandem Mass Spectra Using the DRIP Toolkit: Training, Searching, and Post-ProcessingJohn T Halloran
Advances in Neural Information Processing Systems|November 21, 2019
Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass SpectraJohn T Halloran, David M Rocke
Advances in Neural Information Processing Systems|November 21, 2019
Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass SpectraJohn T Halloran, David M Rocke
Journal of Proteome Research|April 3, 2018
A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale ProteomicsJohn T Halloran, David M Rocke
Uncertainty in Artificial Intelligence : Proceedings of the ... Conference. Conference on Uncertainty in Artificial Intelligence|October 10, 2014
Learning Peptide-Spectrum Alignment Models for Tandem Mass SpectrometryJohn T Halloran, Jeff A Bilmes, William S Noble
Journal of Proteome Research|July 12, 2016
Dynamic Bayesian Network for Accurate Detection of Peptides from Tandem Mass SpectraJohn T Halloran, Jeff A Bilmes, William S Noble
Bioinformatics (Oxford, England)|June 17, 2016
Faster and more accurate graphical model identification of tandem mass spectra using trellisesShengjie Wang, John T Halloran, Jeff A Bilmes, et al.
Journal of Proteome Research|August 14, 2019
Speeding Up PercolatorJohn T Halloran, Hantian Zhang, Kaan Kara, et al.
Scientific Reports|December 7, 2017
Comprehensive statistical inference of the clonal structure of cancer from multiple biopsiesJie Liu, John T Halloran, Jeffrey A Bilmes, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
Methods in Molecular Biology (Clifton, N.J.)|July 22, 2018
Analyzing Tandem Mass Spectra Using the DRIP Toolkit: Training, Searching, and Post-ProcessingJohn T Halloran
Advances in Neural Information Processing Systems|November 21, 2019
Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass SpectraJohn T Halloran, David M Rocke
Advances in Neural Information Processing Systems|November 21, 2019
Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass SpectraJohn T Halloran, David M Rocke
Journal of Proteome Research|April 3, 2018
A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale ProteomicsJohn T Halloran, David M Rocke
Uncertainty in Artificial Intelligence : Proceedings of the ... Conference. Conference on Uncertainty in Artificial Intelligence|October 10, 2014
Learning Peptide-Spectrum Alignment Models for Tandem Mass SpectrometryJohn T Halloran, Jeff A Bilmes, William S Noble
Journal of Proteome Research|July 12, 2016
Dynamic Bayesian Network for Accurate Detection of Peptides from Tandem Mass SpectraJohn T Halloran, Jeff A Bilmes, William S Noble
Bioinformatics (Oxford, England)|June 17, 2016
Faster and more accurate graphical model identification of tandem mass spectra using trellisesShengjie Wang, John T Halloran, Jeff A Bilmes, et al.
Journal of Proteome Research|August 14, 2019
Speeding Up PercolatorJohn T Halloran, Hantian Zhang, Kaan Kara, et al.
Scientific Reports|December 7, 2017
Comprehensive statistical inference of the clonal structure of cancer from multiple biopsiesJie Liu, John T Halloran, Jeffrey A Bilmes, et al.
Pageof 1