Search Results - "Halloran, John T."

Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies by Liu, Jie, Halloran, John T., Bilmes, Jeffrey A., Daza, Riza M., Lee, Choli, Mahen, Elisabeth M., Prunkard, Donna, Song, Chaozhong, Blau, Sibel, Dorschner, Michael O., Gadi, Vijayakrishna K., Shendure, Jay, Blau, C. Anthony, Noble, William S.

“…A comprehensive characterization of tumor genetic heterogeneity is critical for understanding how cancers evolve and escape treatment. Although many algorithms…”
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A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics by Halloran, John T, Rocke, David M

“…Percolator is an important tool for greatly improving the results of a database search and subsequent downstream analysis. Using support vector machines…”
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Dynamic Bayesian Network for Accurate Detection of Peptides from Tandem Mass Spectra by Halloran, John T, Bilmes, Jeff A, Noble, William S

“…A central problem in mass spectrometry analysis involves identifying, for each observed tandem mass spectrum, the corresponding generating peptide. We present…”
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Speeding Up Percolator by Halloran, John T, Zhang, Hantian, Kara, Kaan, Renggli, Cédric, The, Matthew, Zhang, Ce, Rocke, David M, Käll, Lukas, Noble, William Stafford

“…The processing of peptide tandem mass spectrometry data involves matching observed spectra against a sequence database. The ranking and calibration of these…”
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Faster and more accurate graphical model identification of tandem mass spectra using trellises by Wang, Shengjie, Halloran, John T, Bilmes, Jeff A, Noble, William S

“…Tandem mass spectrometry (MS/MS) is the dominant high throughput technology for identifying and quantifying proteins in complex biological samples. Analysis of…”
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Graphical Models for Peptide Identification of Tandem Mass Spectra by Halloran, John T

“…Graphical models (GMs) provide a flexible framework for modeling phenomena. In the past few decades, GMs have become indispensable tools for machine learning…”
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Mamba State-Space Models Are Lyapunov-Stable Learners by Halloran, John T, Gulati, Manbir, Roysdon, Paul F

“…Mamba state-space models (SSMs) were recently shown to outperform state-of-the-art (SOTA) Transformer large language models (LLMs) across various tasks…”
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GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification by Halloran, John T, Rocke, David M

“…One of the most efficient methods to solve L2-regularized primal problems, such as logistic regression and linear support vector machine (SVM) classification,…”
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Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra by Halloran, John T, Rocke, David M

“…Tandem mass spectrometry (MS/MS) is a high-throughput technology used toidentify the proteins in a complex biological sample, such as a drop of blood. A…”
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Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra by Halloran, John T, Rocke, David M

“…The most widely used technology to identify the proteins present in a complex biological sample is tandem mass spectrometry, which quickly produces a large…”
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Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization by Iyer, Rishabh, Halloran, John T, Wei, Kai

“…This paper introduces Jensen, an easily extensible and scalable toolkit for production-level machine learning and convex optimization. Jensen implements a…”
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Graphical Models for Peptide Identification of Tandem Mass Spectra by Halloran, John T

“…Graphical models (GMs) provide a flexible framework for modeling phenomena. In the past few decades, GMs have become indispensable tools for machine learning…”
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Faster graphical model identification of tandem mass spectra using peptide word lattices by Wang, Shengjie, Halloran, John T, Bilmes, Jeff A, Noble, William S

“…Liquid chromatography coupled with tandem mass spectrometry, also known as shotgun proteomics, is a widely-used high-throughput technology for identifying…”
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