William Stafford Noble

发表

Constructing Structural Profiles for Protein Torsion Angle Prediction

Zafer Aydin, David Baker, William Stafford Noble, 2015, BIOINFORMATICS.

Combining High-Resolution and Exact Calibration To Boost Statistical Power: A Well-Calibrated Score Function for High-Resolution MS2 Data.

William Stafford Noble, Andy Lin, J Jeffry Howbert, 2018, Journal of proteome research.

Learning Models of Biological Sequences

William Stafford Noble, Christina S. Leslie, 2010, Encyclopedia of Machine Learning.

Epigenetic priors for identifying active transcription factor binding sites

William Stafford Noble, Fabian A. Buske, Timothy L. Bailey, 2012, Bioinform..

Learning Models of Biological Sequences

William Stafford Noble, Christina S. Leslie, C. Leslie, 2010, Encyclopedia of Machine Learning.

Peptide charge state determination for low-resolution tandem mass spectra

William Stafford Noble, Michael J. MacCoss, Christine C. Wu, 2005, 2005 IEEE Computational Systems Bioinformatics Conference (CSB'05).

A new pairwise kernel for biological network inference with support vector machines

William Stafford Noble, Jean-Philippe Vert, Jian Qiu, 2007, BMC Bioinformatics.

Ledidi: Designing genomic edits that induce functional activity

William Stafford Noble, Jacob M. Schreiber, Jacob Schreiber, 2020, bioRxiv.

Improved False Discovery Rate Estimation Procedure for Shotgun Proteomics

William Stafford Noble, Uri Keich, Attila Kertesz-Farkas, 2015, Journal of proteome research.

Exploring Gene Expression Data with Class Scores

William Stafford Noble, Paul Pavlidis, Darrin P. Lewis, 2001, Pacific Symposium on Biocomputing.

Protein Interaction Networks: Protein Domain Interaction and Protein Function Prediction

Yanjun Qi, William Stafford Noble, Yanjun Qi, 2011, Handbook of Statistical Bioinformatics.

Predicting Nucleosome Positioning Using Multiple Evidence Tracks

Jeff A. Bilmes, William Stafford Noble, Zhiping Weng, 2010, RECOMB.

Unsupervised manifold alignment for single-cell multi-omics data

William Stafford Noble, He Fang, Jay Shendure, 2020, BCB.

Combining high resolution and exact calibration to boost statistical power: A well-calibrated score function for high-resolution MS2 data

William Stafford Noble, Andy Lin, J. Jeffry Howbert, 2018, bioRxiv.

Detecting Remote Evolutionary Relationships among Proteins by Large-Scale Semantic Embedding

Jason Weston, William Stafford Noble, Christina S. Leslie, 2011, PLoS Comput. Biol..

Kernel hierarchical gene clustering from microarray expression data

William Stafford Noble, Jie Qin, Darrin P. Lewis, 2003, Bioinform..

MoMo: Discovery of post-translational modification motifs

William Stafford Noble, Timothy L. Bailey, Alice Cheng, 2017, bioRxiv.

A statistical framework for genomic data fusion

Nello Cristianini, Michael I. Jordan, Tijl De Bie, 2004, Bioinform..

Non-parametric estimation of posterior error probabilities associated with peptides identified by tandem mass spectrometry

William Stafford Noble, Lukas Käll, John D. Storey, 2008, ECCB.

Submodular Maximization via Gradient Ascent: The Case of Deep Submodular Functions

Jeff A. Bilmes, William Stafford Noble, Wenruo Bai, 2018, NeurIPS.

Extremely fast and accurate open modification spectral library searching of high-resolution mass spectra using feature hashing and graphics processing units

Wout Bittremieux, William Stafford Noble, Kris Laukens, 2019, bioRxiv.

Support vector machine classification on the web

William Stafford Noble, Paul Pavlidis, Ilan Wapinski, 2004, Bioinform..

DANCE: Enhancing saliency maps using decoys

William Stafford Noble, Yang Lu, Wenbo Guo, 2020, ICML.

Learning Gene Functional Classifications from Multiple Data Types

Jason Weston, William Stafford Noble, Jinsong Cai, 2002, J. Comput. Biol..

Large-scale prediction of protein-protein interactions from structures

William Stafford Noble, Jean-Philippe Vert, Michael Riffle, 2010, BMC Bioinformatics.

Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra

Jeff A. Bilmes, William Stafford Noble, Katrin Kirchhoff, 2012, UAI.

A Kernel Approach for Learning from Almost Orthogonal Patterns

Bernhard Schölkopf, Jason Weston, Eleazar Eskin, 2002, European Conference on Principles of Data Mining and Knowledge Discovery.

Data hoarding is harming proteomics

William Stafford Noble, 2004, Nature Biotechnology.

DeepPINK: reproducible feature selection in deep neural networks

William Stafford Noble, Yingying Fan, Yang Young Lu, 2018, NeurIPS.

Multi-class protein fold recognition using adaptive codes

Jason Weston, William Stafford Noble, Christina S. Leslie, 2005, ICML.

Efficient identification of DNA hybridization partners in a sequence database

William Stafford Noble, Tobias P. Mann, 2006, ISMB.

apricot: Submodular selection for data summarization in Python

William Stafford Noble, Jacob M. Schreiber, Jacob Schreiber, 2019, J. Mach. Learn. Res..

Jointly Embedding Multiple Single-Cell Omics Measurements

William Stafford Noble, Jean-Philippe Vert, Jie Liu, 2019, bioRxiv.

Fast open modification spectral library searching through approximate nearest neighbor indexing

Wout Bittremieux, William Stafford Noble, Kris Laukens, 2018, bioRxiv.

Estimating relative abundances of proteins from shotgun proteomics data

William Stafford Noble, Michael J. MacCoss, Michael S. Bereman, 2012, BMC Bioinformatics.

The effect of replication on gene expression microarray experiments

William Stafford Noble, Paul Pavlidis, Qinghong Li, 2003, Bioinform..

Crux: Rapid Open Source Protein Tandem Mass Spectrometry Analysis

William Stafford Noble, Barbara Frewen, Michael J. MacCoss, 2014, Journal of proteome research.

Controlling the FDR in Imperfect Matches to an Incomplete Database

William Stafford Noble, Uri Keich, U. Keich, 2018, Journal of the American Statistical Association.

Dealing with large diagonals in kernel matrices

Jason Weston, William Stafford Noble, Bernhard Schölkopf, 2003 .

Submodular sketches of single-cell RNA-seq measurements

William Stafford Noble, Wei Yang, Jeffrey Bilmes, 2020, BCB.

Inferring diploid 3D chromatin structures from Hi-C data

William Stafford Noble, Jean-Philippe Vert, Nelle Varoquaux, 2019, bioRxiv.

Jointly Embedding Multiple Single-Cell Omics Measurements

William Stafford Noble, Jean-Philippe Vert, Jie Liu, 2019, bioRxiv.

Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast

Nello Cristianini, Michael I. Jordan, William Stafford Noble, 2003, Pacific Symposium on Biocomputing.

Computational and Statistical Analysis of Protein Mass Spectrometry Data

William Stafford Noble, Michael J. MacCoss, M. MacCoss, 2012, PLoS Comput. Biol..

Learning to predict protein-protein interactions from protein sequences

William Stafford Noble, Andrey Rzhetsky, Shawn M. Gomez, 2003, Bioinform..

Multi-class Protein Classification Using Adaptive Codes

Jason Weston, William Stafford Noble, Christina S. Leslie, 2007, J. Mach. Learn. Res..

Ten simple rules for writing a response to reviewers

William Stafford Noble, 2017, PLoS Comput. Biol..

Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks

Jeff A. Bilmes, William Stafford Noble, Lukas Käll, 2008, PLoS Comput. Biol..

Choosing negative examples for the prediction of protein-protein interactions

William Stafford Noble, Asa Ben-Hur, A. Ben-Hur, 2006, BMC Bioinformatics.

HiC-Spector: A matrix library for spectral and reproducibility analysis of Hi-C contact maps

Mark Gerstein, William Stafford Noble, Koon-Kiu Yan, 2016 .

A machine learning strategy that leverages large datasets to boost statistical power in small-scale experiments

William Stafford Noble, William E. Fondrie, 2019, bioRxiv.

SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

Jason Weston, William Stafford Noble, Christina S. Leslie, 2007, BMC Bioinformatics.

Rankprop: a web server for protein remote homology detection

Jason Weston, William Stafford Noble, Christina S. Leslie, 2008, Bioinform..

Progressive Calibration and Averaging for Tandem Mass Spectrometry Statistical Confidence Estimation: Why Settle for a Single Decoy?

William Stafford Noble, Uri Keich, U. Keich, 2017, RECOMB.

Exploratory analysis of genomic segmentations with Segtools

William Stafford Noble, Orion J. Buske, Karine G. Le Roch, 2011, BMC Bioinformatics.

Combining Pairwise Sequence Similarity and Support Vector Machines for Detecting Remote Protein Evolutionary and Structural Relationships

Li Liao, William Stafford Noble, Li Liao, 2003, J. Comput. Biol..

The Structure and Function of Chromatin and Chromosomes

William Stafford Noble, Yi Mao, C. Anthony Blau, 2011, Pacific Symposium on Biocomputing.

Prioritizing transcriptomic and epigenomic experiments using an optimization strategy that leverages imputed data

Jeff A. Bilmes, William Stafford Noble, Jacob M. Schreiber, 2019, bioRxiv.

Unsupervised segmentation of continuous genomic data

William Stafford Noble, Robert E. Thurman, John A. Stamatoyannopoulos, 2007, Bioinform..

Unsupervised pattern discovery in human chromatin structure through genomic segmentation

Jie Wang, Jeff A. Bilmes, William Stafford Noble, 2013, BCB.

Fast Open Modification Spectral Library Searching through Approximate Nearest Neighbor Indexing.

Wout Bittremieux, William Stafford Noble, Kris Laukens, 2018, Journal of proteome research.

Multi-Scale Correlations in Continuous Genomic Data

William Stafford Noble, Robert E. Thurman, John A. Stamatoyannopoulos, 2007, Pacific Symposium on Biocomputing.

Automated validation of polymerase chain reactions using amplicon melting curves

William Stafford Noble, John A. Stamatoyannopoulos, Tobias P. Mann, 2005, 2005 IEEE Computational Systems Bioinformatics Conference (CSB'05).

Protein Family Classification Using Sparse Markov Transducers

Eleazar Eskin, Yoram Singer, William Stafford Noble, 2000, J. Comput. Biol..

Entropic Graph-based Posterior Regularization

Jeff A. Bilmes, William Stafford Noble, Maxwell W. Libbrecht, 2015, ICML.

Learning score function parameters for improved spectrum identification in tandem mass spectrometry experiments.

William Stafford Noble, Michael S Bereman, Michael S. Bereman, 2012, Journal of proteome research.

Classification of genes using probabilistic models of microarray expression profiles

William Stafford Noble, Paul Pavlidis, Christopher Tang, 2001, BIOKDD.

A practically efficient graph-theoretic approach to protein identification in mass spectrometry

Oliver Serang, William Stafford Noble, 2011 .

Matrix2png: a utility for visualizing matrix data

William Stafford Noble, Paul Pavlidis, P. Pavlidis, 2003, Bioinform..

Detecting modifications in proteomics experiments with Param-Medic

William Stafford Noble, Kaipo Tamura, Damon May, 2018 .

Robust saliency maps with distribution-preserving decoys

William Stafford Noble, Yang Young Lu, Wenbo Guo, 2019 .

Mismatch String Kernels for SVM Protein Classification

Jason Weston, Eleazar Eskin, William Stafford Noble, 2002, NIPS.

Kernel methods for predicting protein-protein interactions

William Stafford Noble, Asa Ben-Hur, A. Ben-Hur, 2005, ISMB.

Automated Validation of Polymerase Chain Reaction Amplicon Melting Curves

William Stafford Noble, John A. Stamatoyannopoulos, Tobias P. Mann, 2006, J. Bioinform. Comput. Biol..

Predicting Co-Complexed Protein Pairs from Heterogeneous Data

William Stafford Noble, Jian Qiu, Jian Qiu, 2008, PLoS Comput. Biol..

Robust saliency maps with decoy-enhanced saliency score pdf

William Stafford Noble, Yang Lu, Wenbo Guo, 2020, ArXiv.

Multiple Competition-Based FDR Control and Its Application to Peptide Detection

William Stafford Noble, Uri Keich, Kristen Emery, 2020, RECOMB.

Unsupervised embedding of single-cell Hi-C data

William Stafford Noble, Jie Liu, Galip Gürkan Yardımcı, 2018 .

Semi-supervised Protein Classification Using Cluster Kernels

Jason Weston, William Stafford Noble, Christina S. Leslie, 2003, NIPS.

Peptide Retention Time Prediction Yields Improved Tandem Mass Spectrum Identification for Diverse Chromatography Conditions

William Stafford Noble, Michael J. MacCoss, Aaron A. Klammer, 2007, RECOMB.

Kernels for gene regulatory regions

William Stafford Noble, Jean-Philippe Vert, Robert E. Thurman, 2005, NIPS.

PECAN: Library Free Peptide Detection for Data-Independent Acquisition Tandem Mass Spectrometry Data

Samuel H Payne, Michael J MacCoss, William Stafford Noble, 2017, Nature Methods.

Multi-scale deep tensor factorization learns a latent representation of the human epigenome

Jeff A. Bilmes, William Stafford Noble, Timothy Durham, 2018, bioRxiv.

Improved network-based identification of protein orthologs

Roded Sharan, William Stafford Noble, Nir Yosef, 2008, ECCB.

Inferring Clonal Composition from Multiple Sections of a Breast Cancer

Junfeng Wang, William Stafford Noble, Daniela Witten, 2014, PLoS Comput. Biol..

Segway 2.0: Gaussian mixture models and minibatch training

William Stafford Noble, Maxwell W. Libbrecht, Michael M. Hoffman, 2017 .

Predicting the in vivo signature of human gene regulatory sequence

William Stafford Noble, Robert E. Thurman, John A. Stamatoyannopoulos, 2005, ISMB.

Segway 2.0 Application Note Scripts

Jeff A. Bilmes, William Stafford Noble, Maxwell W. Libbrecht, 2017 .

Semi-supervised Protein Classification Using Cluster Kernels

Jason Weston, William Stafford Noble, Christina S. Leslie, 2003, NIPS.

Choosing non‐redundant representative subsets of protein sequence data sets using submodular optimization

William Stafford Noble, Maxwell W Libbrecht, Jeffrey A Bilmes, 2018, Proteins.

PREDICTD PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition

William Stafford Noble, J Jeffry Howbert, Maxwell W Libbrecht, 2018, Nature Communications.

Choosing non‐redundant representative subsets of protein sequence data sets using submodular optimization

William Stafford Noble, Jeffrey A. Bilmes, Maxwell W. Libbrecht, 2018, BCB.

Structural bioinformatics Semi-supervised protein classification using cluster kernels

Jason Weston, William Stafford Noble, André Elisseeff, 2005 .

Mismatch string kernels for discriminative protein classification

Jason Weston, Eleazar Eskin, William Stafford Noble, 2004, Bioinform..

A Kernel Approach for Learning from Almost Orthogonal Patterns

Bernhard Schölkopf, Jason Weston, Eleazar Eskin, 2002, PKDD.

Prioritizing transcriptomic and epigenomic experiments by using an optimization strategy that leverages imputed data.

William Stafford Noble, Jacob Schreiber, Jeffrey Bilmes, 2020, Bioinformatics.

MetaGOmics: A Web-Based Tool for Peptide-Centric Functional and Taxonomic Analysis of Metaproteomics Data

William Stafford Noble, Michael Riffle, Daniel Jaschob, 2017, Proteomes.

Ranking predicted protein structures with support vector regression

David Baker, William Stafford Noble, Jian Qiu, 2007, Proteins.

Using Substitution Matrices to Estimate Probability Distributions for Biological Sequences

Eleazar Eskin, Yoram Singer, William Stafford Noble, 2002, J. Comput. Biol..

Guest Editors' Introduction to the Special Issue: Machine Learning for Bioinformatics-Part 1

William Stafford Noble, Charles X. Ling, Qiang Yang, 2005, TCBB.

Prediction of Phenotype Information from Genotype Data

Roded Sharan, Richard M. Karp, William Stafford Noble, 2010, Commun. Inf. Syst..

A Quick Guide to Organizing Computational Biology Projects

William Stafford Noble, 2009, PLoS Comput. Biol..

Faster Mass Spectrometry-Based Protein Inference: Junction Trees Are More Efficient than Sampling and Marginalization by Enumeration

William Stafford Noble, Oliver Serang, O. Serang, 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Unsupervised embedding of single-cell Hi-C data

Jie Liu, William Stafford Noble, Galip Gürkan Yardimci, 2018, Bioinform..

Genomic data visualization on the Web

Wei Wu, William Stafford Noble, 2004, Bioinform..

Segway 2.0: Gaussian mixture models and minibatch training

William Stafford Noble, Jeffrey A. Bilmes, Maxwell W. Libbrecht, 2017, bioRxiv.

MoMo: Discovery of statistically significant post-translational modification motifs

William Stafford Noble, Timothy L. Bailey, Charles E. Grant, 2018 .

A Three-Dimensional Model of the Yeast Genome

William Stafford Noble, Yoo Jung Kim, Jay Shendure, 2011, RECOMB.

The Genomedata format for storing large-scale functional genomics data

William Stafford Noble, Orion J. Buske, Michael M. Hoffman, 2010, Bioinform..

Motif-based protein ranking by network propagation

Jason Weston, William Stafford Noble, Christina S. Leslie, 2005, Bioinform..

William Stafford Noble, Emi Tanaka, Uri Keich, 2011, Bioinform..

Submodular Generalized Matching for Peptide Identification in Tandem Mass Spectrometry

William Stafford Noble, Wenruo Bai, Jeffrey Bilmes, 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

A structural alignment kernel for protein structures

William Stafford Noble, Jean-Philippe Vert, Asa Ben-Hur, 2007, Bioinform..

A Unified Multitask Architecture for Predicting Local Protein Properties

Yanjun Qi, Jason Weston, William Stafford Noble, 2012, PloS one.

Gromov-Wasserstein optimal transport to align single-cell multi-omics data

William Stafford Noble, Björn Sandstede, Ritambhara Singh, 2020, bioRxiv.

MEME Suite: tools for motif discovery and searching

Mikael Bodén, William Stafford Noble, Martin C. Frith, 2009, Nucleic Acids Res..

A learned comparative expression measure for Affymetrix genechip DNA microarrays

Eli Upfal, William Stafford Noble, Will Sheffler, 2005, 2005 IEEE Computational Systems Bioinformatics Conference (CSB'05).

A dynamic Bayesian network for identifying protein-binding footprints from single molecule-based sequencing data

Jeff A. Bilmes, Xiaoyu Chen, William Stafford Noble, 2010, Bioinform..

A statistical approach for inferring the three-dimensional structure of the genome

William Stafford Noble, Jean-Philippe Vert, Ferhat Ay, 2014 .

On the Importance of Well-Calibrated Scores for Identifying Shotgun Proteomics Spectra

William Stafford Noble, Uri Keich, U. Keich, 2014, Journal of proteome research.

Protein Ranking by Semi-Supervised Network Propagation

Jason Weston, William Stafford Noble, Christina S. Leslie, 2006, BMC Bioinformatics.

A cross-validation scheme for machine learning algorithms in shotgun proteomics

William Stafford Noble, Lukas Käll, Viktor Granholm, 2012, BMC Bioinformatics.

Template Scoring Methods for Protein Torsion Angle Prediction

Zafer Aydin, David Baker, William Stafford Noble, 2015, BIOSTEC.

The Spectrum Kernel: A String Kernel for SVM Protein Classification

Eleazar Eskin, William Stafford Noble, Christina S. Leslie, 2001, Pacific Symposium on Biocomputing.

Support vector machine software

William Stafford Noble, 2005 .

Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure

Jeff A. Bilmes, Zafer Aydin, William Stafford Noble, 2011, BMC Bioinformatics.

High Resolution Models of Transcription Factor-DNA Affinities Improve In Vitro and In Vivo Binding Predictions

William Stafford Noble, Christina S. Leslie, William Chang, 2010, PLoS Comput. Biol..

Averaging Strategy To Reduce Variability in Target-Decoy Estimates of False Discovery Rate.

William Stafford Noble, Uri Keich, Kaipo Tamura, 2019, Journal of proteome research.

HiCRep.py: fast comparison of Hi-C contact matrices in Python

William Stafford Noble, Dejun Lin, Justin Sanders, 2020, bioRxiv.

A learned embedding for efficient joint analysis of millions of mass spectra

Jeff A. Bilmes, William Stafford Noble, Damon May, 2018, Nature Methods.

Machine Learning in Computational Biology

Gunnar Rätsch, William Stafford Noble, Gal Chechik, 2007, NIPS 2007.

Empirical comparison of web‐based antimicrobial peptide prediction tools

William Stafford Noble, Musa Nur Gabere, M. Gabere, 2017, Bioinform..

Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry

Jeff A. Bilmes, William Stafford Noble, John T. Halloran, 2014, UAI.

Metric learning pairwise kernel for graph inference pdf

William Stafford Noble, Jean-Philippe Vert, Jian Qiu, 2006, ArXiv.

Learning a Weighted Sequence Model of the Nucleosome Core and Linker Yields More Accurate Predictions in Saccharomyces cerevisiae and Homo sapiens

Jeff A. Bilmes, William Stafford Noble, Sheila M. Reynolds, 2010, PLoS Comput. Biol..

Automated mapping of large-scale chromatin structure in ENCODE

William Stafford Noble, Heng Lian, Robert E. Thurman, 2008, Bioinform..

Inferring Diploid 3D Chromatin Structures from Hi-C Data

William Stafford Noble, Jean-Philippe Vert, Nelle Varoquaux, 2019, WABI.

Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure

William Stafford Noble, Tony Jebara, Darrin P. Lewis, 2006, Bioinform..

qvality: non-parametric estimation of q-values and posterior error probabilities

William Stafford Noble, Lukas Käll, John D. Storey, 2009, Bioinform..

Submodular sketches of single-cell RNA-seq measurements

William Stafford Noble, Wei Yang, Jacob M. Schreiber, 2020, bioRxiv.

Learning kernels from biological networks by maximizing entropy

William Stafford Noble, Koji Tsuda, K. Tsuda, 2004, ISMB/ECCB.

Predictive model of 3D domain formation via CTCF-mediated extrusion

William Stafford Noble, Galip Gürkan Yardimci, G. Yardımcı, 2015, Proceedings of the National Academy of Sciences.

Speeding up Percolator.

William Stafford Noble, Ce Zhang, Kaan Kara, 2019, Journal of proteome research.

Segway 2.0 Application Note Datasets

Jeff A. Bilmes, William Stafford Noble, Maxwell W. Libbrecht, 2017 .

Assessing phylogenetic motif models for predicting transcription factor binding sites

John Hawkins, William Stafford Noble, Timothy L. Bailey, 2009, Bioinform..

Predicting gene expression in the human malaria parasite Plasmodium falciparum using histone modification, nucleosome positioning, and 3D localization features

William Stafford Noble, Karine G. Le Roch, Kate Cook, 2019, PLoS Comput. Biol..

A statistical approach for inferring the 3D structure of the genome

William Stafford Noble, Jean-Philippe Vert, Ferhat Ay, 2014, Bioinform..

DNA sequence+shape kernel enables alignment-free modeling of transcription factor binding

William Stafford Noble, Lin Yang, Remo Rohs, 2016, bioRxiv.

Using machine learning to speed up manual image annotation: application to a 3D imaging protocol for measuring single cell gene expression in the developing C. elegans embryo

Zafer Aydin, William Stafford Noble, Robert H. Waterston, 2010, BMC Bioinformatics.

Capturing cell type-specific chromatin structural patterns by applying topic modeling to single-cell Hi-C data

Jie Liu, William Stafford Noble, Jay Shendure, 2019, bioRxiv.

Extremely fast and accurate open modification spectral library searching of high-resolution mass spectra using feature hashing and graphics processing units

Wout Bittremieux, William Stafford Noble, Kris Laukens, 2019 .

Guest Editors' Introduction to the Special Issue: Machine Learning for Bioinformatics - Part 1

Qiang Yang, William Stafford Noble, Charles X. Ling, 2005, IEEE ACM Trans. Comput. Biol. Bioinform..

FIMO: scanning for occurrences of a given motif

William Stafford Noble, Timothy L. Bailey, Charles E. Grant, 2011, Bioinform..

GenomeDISCO: A concordance score for chromosome conformation capture experiments using random walks on contact map graphs

William Stafford Noble, Anshul Kundaje, Oana Ursu, 2017, bioRxiv.

Combining pairwise sequence similarity and support vector machines for remote protein homology detection

Li Liao, William Stafford Noble, Li Liao, 2002, RECOMB '02.

MCAST: scanning for cis-regulatory motif clusters

William Stafford Noble, Timothy L. Bailey, James Johnson, 2016, Bioinform..

Correction to "Improved False Discovery Rate Estimation Procedure for Shotgun Proteomics".

William Stafford Noble, Uri Keich, Attila Kertesz-Farkas, 2016, Journal of proteome research.

Combining classifiers for improved classification of proteins from sequence or structure

Jason Weston, William Stafford Noble, Christina S. Leslie, 2008, BMC Bioinformatics.

Guest Editor's Introduction to the Special Issue: Machine Learning for Bioinformatics-Part 2

Qiang Yang, William Stafford Noble, Charles X. Ling, 2005, TCBB.

NIPS workshop on New Problems and Methods in Computational Biology

Gunnar Rätsch, William Stafford Noble, Gal Chechik, 2007, BMC Bioinformatics.

Unsupervised manifold alignment for single-cell multi-omics data

William Stafford Noble, Ritambhara Singh, Pinar Demetci, 2020, bioRxiv.

Nonstationary kernel combination

William Stafford Noble, Tony Jebara, Darrin P. Lewis, 2006, ICML.

On the Relationship between DNA Periodicity and Local Chromatin Structure

Jeff A. Bilmes, William Stafford Noble, Sheila M. Reynolds, 2009, RECOMB.

Predicting Human Nucleosome Occupancy from Primary Sequence

William Stafford Noble, Jonathan H. Dennis, Robert E. Thurman, 2008, PLoS Comput. Biol..

Protein ranking: from local to global structure in the protein similarity network.

Jason Weston, William Stafford Noble, Dengyong Zhou, 2004, Proceedings of the National Academy of Sciences of the United States of America.

Session Introduction

William Stafford Noble, Manolis Kellis, Alexander J. Hartemink, 2003, Pacific Symposium on Biocomputing.

GenomeDISCO: A concordance score for chromosome conformation capture experiments using random walks on contact map graphs

William Stafford Noble, Anshul Kundaje, Oana Ursu, 2017 .

Response to comments on 'Empirical comparison of web-based antimicrobial peptide prediction tools'

William Stafford Noble, Musa Nur Gabere, 2019, Bioinform..

Modeling peptide fragmentation with dynamic Bayesian networks for peptide identification

Jeff A. Bilmes, William Stafford Noble, Michael J. MacCoss, 2008, ISMB.

Searching for statistically significant regulatory modules

William Stafford Noble, Timothy L. Bailey, T. Bailey, 2003, ECCB.

Improving the Comprehensiveness of Large-Scale Proteomics Experiments Using Advanced Computational Tools and Accurate Multiple Hypothesis Testing Statistics

William Stafford Noble, Barbara Frewen, Michael J. MacCoss, 2011 .

DIAmeter: matching peptides to data-independent acquisition mass spectrometry data

William Stafford Noble, Judit Villén, Jeff Bilmes, 2021, Bioinform..

ACE: Explaining cluster from an adversarial perspective

William Stafford Noble, Yang Young Lu, Giancarlo Bonora, 2021, ICML.

DANCE: Enhancing saliency maps using decoys

William Stafford Noble, Xinyu Xing, Wenbo Guo, 2021, ICML.

Ledidi: Designing genomic edits that induce functional activity

William Stafford Noble, Jacob Schreiber, Yang Young Lu, 2021 .

Consistent probabilistic outputs for protein function prediction

Michael I. Jordan, William Stafford Noble, Gert R. G. Lanckriet, 2008, Genome Biology.

A critical assessment of Mus musculus gene function prediction using integrated genomic evidence

Michael I. Jordan, William Stafford Noble, Gert R. G. Lanckriet, 2008, Genome Biology.

Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project

William Stafford Noble, Nancy R. Zhang, Stephen C. J. Parker, 2007, Nature.

Assessing computational tools for the discovery of transcription factor binding sites

William Stafford Noble, Chun Jimmie Ye, Z. Weng, 2005, Nature Biotechnology.

Machine learning applications in genetics and genomics

William Stafford Noble, Maxwell W. Libbrecht, 2015, Nature Reviews Genetics.

Identifying remote protein homologs by network propagation

William Stafford Noble, J. Weston, C. Leslie, 2005, The FEBS journal.

Integrative annotation of chromatin elements from ENCODE data

William Stafford Noble, Maxwell W. Libbrecht, Robert S. Harris, 2012, Nucleic acids research.

Joint annotation of chromatin state and chromatin conformation reveals relationships among domain types and identifies domains of cell-type-specific expression

William Stafford Noble, Maxwell W. Libbrecht, J. Bilmes, 2014, bioRxiv.

Unsupervised pattern discovery in human chromatin structure through genomic segmentation

William Stafford Noble, Orion J. Buske, J. Bilmes, 2012, Nature Methods.

An Integrated Encyclopedia of DNA Elements in the Human Genome

Raymond K. Auerbach, William Stafford Noble, Stephen C. J. Parker, 2012, Nature.

The ENCODE (ENCyclopedia Of DNA Elements) Project

Paul T. Groth, William Stafford Noble, Colin N. Dewey, 2004, Science.

Semi-supervised learning for peptide identification from shotgun proteomics datasets

William Stafford Noble, J. Weston, M. MacCoss, 2007, Nature Methods.

Quantifying similarity between motifs

William Stafford Noble, J. Stamatoyannopoulos, T. Bailey, 2007, Genome Biology.

Integrating Information for Protein Function Prediction

William Stafford Noble, A. Ben-Hur, 2008 .

Comparative analysis of metazoan chromatin

William Stafford Noble, Stephen C. J. Parker, Maxwell W. Libbrecht, 2014 .

A Framework for Genomic Data Fusion and its Application to Membrane Protein Prediction

Michael I. Jordan, Tijl De Bie, Nello Cristianini, 2004 .

Massively multiplex single-cell Hi-C

William Stafford Noble, J. Shendure, K. Gunderson, 2016, Nature Methods.

Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors

William Stafford Noble, Melissa C. Greven, E. Birney, 2012, Genome research.

Sequence and chromatin determinants of cell-type–specific transcription factor binding

William Stafford Noble, C. Leslie, P. Agius, 2012, Genome research.

Assigning significance to peptides identified by tandem mass spectrometry using decoy databases.

William Stafford Noble, John D. Storey, M. MacCoss, 2008, Journal of proteome research.

The Forkhead transcription factor Hcm1 regulates chromosome segregation genes and fills the S-phase gap in the transcriptional circuitry of the cell cycle.

William Stafford Noble, L. Breeden, T. Pramila, 2006, Genes & development.

Global mapping of protein-DNA interactions in vivo by digital genomic footprinting

William Stafford Noble, Shane J. Neph, Alex P. Reynolds, 2009, Nature Methods.

Changes in genome organization of parasite-specific gene families during the Plasmodium transmission stages

William Stafford Noble, Kate B. Cook, Jean-Philippe Vert, 2018, Nature Communications.

Three-dimensional modeling of the P. falciparum genome during the erythrocytic cycle reveals a strong connection between genome architecture and gene expression

William Stafford Noble, Jean-Philippe Vert, E. Bunnik, 2014, Genome research.

Identifying multi-locus chromatin contacts in human cells using tethered multiple 3C

William Stafford Noble, Michael J. Zeitz, Jean-Philippe Vert, 2015, BMC Genomics.

Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts

William Stafford Noble, T. Bailey, F. Ay, 2014, Genome research.

Bipartite structure of the inactive mouse X chromosome

William Stafford Noble, Andrew J. Hill, J. Shendure, 2015, Genome Biology.

A Three-Dimensional Model of the Yeast Genome

William Stafford Noble, J. Shendure, S. Fields, 2010, Nature.

Identification of higher-order functional domains in the human ENCODE regions.

William Stafford Noble, J. Stamatoyannopoulos, R. Thurman, 2007, Genome research.

Nucleosome positioning signals in genomic DNA.

William Stafford Noble, Z. Weng, J. Stamatoyannopoulos, 2007, Genome research.

Param-Medic: A Tool for Improving MS/MS Database Search Yield by Optimizing Parameter Settings.

William Stafford Noble, Damon H. May, Kaipo Tamura, 2017, Journal of proteome research.

Rapid and accurate peptide identification from tandem mass spectra.

William Stafford Noble, Aaron A. Klammer, M. MacCoss, 2008, Journal of proteome research.

Multiple competition based FDR control

William Stafford Noble, U. Keich, Kristen Emery, 2019 .

Determining the calibration of confidence estimation procedures for unique peptides in shotgun proteomics.

William Stafford Noble, L. Käll, J. F. Navarro, 2013, Journal of proteomics.

Predicting gene expression in the human malaria parasite Plasmodium falciparum

William Stafford Noble, Yang Young Lu, Kate B. Cook, 2018, bioRxiv.

Faster SEQUEST searching for peptide identification from tandem mass spectra.

William Stafford Noble, Benjamin Diament, 2011, Journal of proteome research.

A User's Guide to the Encyclopedia of DNA Elements (ENCODE)

Raymond K. Auerbach, William Stafford Noble, Stephen C. J. Parker, 2011, PLoS biology.

Support vector machine

William Stafford Noble, William S Noble, 2013 .

Nucleotide sequence and DNaseI sensitivity are predictive of 3D chromatin architecture

William Stafford Noble, Maxwell W. Libbrecht, J. Bilmes, 2017, bioRxiv.

HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient

William Stafford Noble, R. Hardison, Feng Yue, 2017, bioRxiv.

Integrative phenomics reveals insight into the structure of phenotypic diversity in budding yeast

William Stafford Noble, Jon Wakefield, Sara J Cooper, 2013, Genome research.

Avocado: a multi-scale deep tensor factorization method learns a latent representation of the human epigenome

William Stafford Noble, Jacob M. Schreiber, Timothy J. Durham, 2020, Genome Biology.

Topologically associating domains and their long-range contacts are established during early G1 coincident with the establishment of the replication-timing program

William Stafford Noble, Daniel L. Vera, William S Noble, 2015, Genome research.

Integrative detection and analysis of structural variation in cancer genomes

William Stafford Noble, Jesse R. Dixon, Royden A. Clark, 2018, Nature Genetics.

Efficient marginalization to compute protein posterior probabilities from shotgun mass spectrometry data.

William Stafford Noble, M. MacCoss, O. Serang, 2010, Journal of proteome research.

Eliminating redundancy among protein sequences using submodular optimization

William Stafford Noble, Maxwell W. Libbrecht, J. Bilmes, 2016, bioRxiv.

Large-scale identification of yeast integral membrane protein interactions.

William Stafford Noble, S. Fields, A. Ben-Hur, 2005, Proceedings of the National Academy of Sciences of the United States of America.

Completing the ENCODE3 compendium yields accurate imputations across a variety of assays and human biosamples

William Stafford Noble, Jacob M. Schreiber, J. Bilmes, 2020, Genome Biology.

Maintaining replication origins in the face of genomic change

William Stafford Noble, William S Noble, Tobias P. Mann, 2012, Genome research.

A unified encyclopedia of human functional DNA elements through fully automated annotation of 164 human cell types

William Stafford Noble, Maxwell W. Libbrecht, J. Bilmes, 2016, bioRxiv.

A unified encyclopedia of human functional DNA elements through fully automated annotation of 164 human cell types

William Stafford Noble, Maxwell W. Libbrecht, J. Bilmes, 2016, Genome Biology.

Choosing panels of genomics assays using submodular optimization

William Stafford Noble, Maxwell W Libbrecht, Kai Wei, 2016, Genome Biology.

Entropic Graph-based Posterior Regularization : Extended Version

William Stafford Noble, Maxwell W. Libbrecht, J. Bilmes, 2015 .

Analysis methods for studying the 3D architecture of the genome

William Stafford Noble, F. Ay, 2015, Genome Biology.

Joint annotation of chromatin state and chromatin conformation reveals relationships among domain types and identifies domains of cell type-specific expression

William Stafford Noble, Maxwell W. Libbrecht, J. Bilmes, 2014, bioRxiv.

Distinct epigenetic features of differentiation-regulated replication origins

William Stafford Noble, Maxwell W. Libbrecht, M. Aladjem, 2016, Epigenetics & Chromatin.

Comprehensive identification and analysis of human accelerated regulatory DNA

William Stafford Noble, L. Pennacchio, F. Ay, 2015, Genome research.

Statistical calibration of the SEQUEST XCorr function.

William Stafford Noble, Aaron A. Klammer, Christopher Y Park, 2009, Journal of proteome research.

Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes

William Stafford Noble, Timothy J. Durham, J. Shendure, 2020, bioRxiv.

Analysis of peptide MS/MS spectra from large-scale proteomics experiments using spectrum libraries.

William Stafford Noble, Gennifer E. Merrihew, M. MacCoss, 2006, Analytical chemistry.

Posterior error probabilities and false discovery rates: two sides of the same coin.

William Stafford Noble, John D. Storey, M. MacCoss, 2008, Journal of proteome research.

Improvements to the percolator algorithm for Peptide identification from shotgun proteomics data sets.

William Stafford Noble, J. Weston, L. Bottou, 2009, Journal of proteome research.

Inferring Clonal Composition from Multiple Sections of a Breast Cancer

William Stafford Noble, D. Witten, D. Witten, 2014, PLoS Comput. Biol..

Motif-based analysis of large nucleotide data sets using MEME-ChIP

William Stafford Noble, T. Bailey, Wenxiu Ma, 2014, Nature Protocols.

Classification of clear-cell sarcoma as a subtype of melanoma by genomic profiling.

William Stafford Noble, A. Viale, C. Antonescu, 2003, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

Classification and subtype prediction of adult soft tissue sarcoma by functional genomics.

William Stafford Noble, Jonathan J. Lewis, C. Antonescu, 2003, The American journal of pathology.

The lncRNA Firre anchors the inactive X chromosome to the nucleolus by binding CTCF and maintains H3K27me3 methylation

William Stafford Noble, J. Shendure, C. Disteche, 2015, Genome Biology.

Sci-Hi-C: a single-cell Hi-C method for mapping 3D genome organization in large number of single cells

William Stafford Noble, J. Shendure, R. Qiu, 2019, bioRxiv.

Single-cell landscape of nuclear configuration and gene expression during stem cell differentiation and X inactivation

William Stafford Noble, Cole Trapnell, J. Shendure, 2020, Genome Biology.

Mapping 3D genome architecture through in situ DNase Hi-C

William Stafford Noble, J. Shendure, R. Qiu, 2016, Nature Protocols.

HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient

William Stafford Noble, R. Hardison, Feng Yue, 2017, bioRxiv.

Using DNase Hi-C techniques to map global and local three-dimensional genome architecture at high resolution

William Stafford Noble, J. Shendure, C. Disteche, 2017, bioRxiv.

Nucleotide sequence and DNaseI sensitivity are predictive of 3D chromatin architecture

William Stafford Noble, Maxwell W. Libbrecht, J. Bilmes, 2017, bioRxiv.

Sci-Hi-C: a single-cell Hi-C method for mapping 3D genome organization in large number of single cells.

William Stafford Noble, J. Shendure, R. Qiu, 2019, Methods.

A pitfall for machine learning methods aiming to predict across cell types

William Stafford Noble, Jacob M. Schreiber, J. Bilmes, 2020, Genome biology.

Mass spectrometrists should search only for peptides they care about

William Stafford Noble, 2015, Nature Methods.

Tandem Mass Spectrum Identification via Cascaded Search

William Stafford Noble, Uri Keich, Attila Kertesz-Farkas, 2015, Journal of proteome research.

Software tools for visualizing Hi-C data

William Stafford Noble, Galip Gürkan Yardımcı, G. Yardımcı, 2017, Genome Biology.

A benchmark of Hi-C scaffolders using reference genomes and de novo assemblies

William Stafford Noble, P. Myler, Aakash Sur, 2022, bioRxiv.

A Deeper Look into Comet—Implementation and Features

Michael J MacCoss, William Stafford Noble, William S Noble, 2015, Journal of The American Society for Mass Spectrometry.

Fast and Accurate Protein False Discovery Rates on Large-Scale Proteomics Data Sets with Percolator 3.0

William Stafford Noble, Michael J. MacCoss, Matthew The, 2016, Journal of The American Society for Mass Spectrometry.

Multiple dimensions of epigenetic gene regulation in the malaria parasite Plasmodium falciparum

William Stafford Noble, Jean-Philippe Vert, Nelle Varoquaux, 2015, BioEssays : news and reviews in molecular, cellular and developmental biology.

The spectrum kernel

Eleazar Eskin, William Stafford Noble, Christina C. Leslie, 2002 .

Measuring significant changes in chromatin conformation with ACCOST

William Stafford Noble, Jean-Philippe Vert, Karine G. Le Roch, 2019, bioRxiv.

Orientation-dependent Dxz4 contacts shape the 3D structure of the inactive X chromosome

William Stafford Noble, J. Shendure, R. Qiu, 2017, Nature Communications.

Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies

William Stafford Noble, John T. Halloran, J. Bilmes, 2017, bioRxiv.

On using samples of known protein content to assess the statistical calibration of scores assigned to peptide-spectrum matches in shotgun proteomics.

William Stafford Noble, Lukas Käll, Viktor Granholm, 2011, Journal of proteome research.

An Integrative Framework for Detecting Structural Variations in Cancer Genomes

William Stafford Noble, Feng Yue, Jesse R. Dixon, 2017, bioRxiv.

A new algorithm for the evaluation of shotgun peptide sequencing in proteomics: support vector machine classification of peptide MS/MS spectra and SEQUEST scores.

William Stafford Noble, D. Payan, Weiqun Li, 2003, Journal of proteome research.

Direct Maximization of Protein Identifications from Tandem Mass Spectra*

William Stafford Noble, J. Weston, M. MacCoss, 2011, Molecular & Cellular Proteomics.

Use of shotgun proteomics for the identification, confirmation, and correction of C. elegans gene annotations.

Michael J MacCoss, William Stafford Noble, Lukas Käll, 2008, Genome research.

Protein Torsion Angle Class Prediction by a Hybrid Architecture of Bayesian and Neural Networks

William Stafford Noble, J. Bilmes, D. Baker, 2012 .

Peptide-Centric Proteome Analysis: An Alternative Strategy for the Analysis of Tandem Mass Spectrometry Data*

Samuel H Payne, Michael J MacCoss, William Stafford Noble, 2015, Molecular & Cellular Proteomics.

On the assessment of statistical significance of three-dimensional colocalization of sets of genomic elements

William Stafford Noble, Daniela M. Witten, D. Witten, 2012, Nucleic acids research.

A thermodynamic approach to PCR primer design

William Stafford Noble, Michael Dorschner, Richard Humbert, 2008, Nucleic acids research.

Proteomic analyses of nucleus laminaris identified candidate targets of the fragile X mental retardation protein

William Stafford Noble, Ying S. Ting, M. MacCoss, 2017, The Journal of comparative neurology.

Measuring the reproducibility and quality of Hi-C data

Mark Gerstein, William Stafford Noble, Anshul Kundaje, 2017, Genome Biology.

A genome-wide 3C-method for characterizing the three-dimensional architectures of genomes.

William Stafford Noble, Jay Shendure, Mirela Andronescu, 2012, Methods.

Fine-scale chromatin interaction maps reveal the cis-regulatory landscape of human lincRNA genes

William Stafford Noble, J. Shendure, C. Disteche, 2014, Nature Methods.

Form and function of topologically associating genomic domains in budding yeast

William Stafford Noble, Ferhat Ay, Umut Eser, 2017, Proceedings of the National Academy of Sciences.

Changes in genome organization of parasite-specific gene families during the Plasmodium transmission stages

William Stafford Noble, Jean-Philippe Vert, David A. Fidock, 2018, bioRxiv.

Accurate identification of centromere locations in yeast genomes using Hi-C

William Stafford Noble, Maitreya J. Dunham, J. Shendure, 2015, Nucleic acids research.

LR-DNase: Predicting TF binding from DNase-seq data

William Stafford Noble, Michael J. Purcaro, Z. Weng, 2016, bioRxiv.

A pitfall for machine learning methods aiming to predict across cell types

Jeff A. Bilmes, William Stafford Noble, Jacob M. Schreiber, 2019, bioRxiv.

Periodic genes of the yeast Saccharomyces cerevisiae: A combined analysis of five cell cycle data sets

William Stafford Noble, Wei Wu, Linda L. Breeden, 2007 .

Capturing cell type-specific chromatin compartment patterns by applying topic modeling to single-cell Hi-C data

William Stafford Noble, J. Shendure, R. Qiu, 2020, PLoS Comput. Biol..

Improving tandem mass spectrum identification using peptide retention time prediction across diverse chromatography conditions.

Michael J MacCoss, William Stafford Noble, Xianhua Yi, 2007, Analytical chemistry.

Dynamic reorganization of nuclear architecture during human cardiogenesis

William Stafford Noble, Jay Shendure, Charles E. Murry, 2017, bioRxiv.

Massively parallel profiling and predictive modeling of the outcomes of CRISPR/Cas9-mediated double-strand break repair

Wei Chen, William Stafford Noble, Jay Shendure, 2018 .

Computing Exact p-values for a Cross-correlation Shotgun Proteomics Score Function

William Stafford Noble, J Jeffry Howbert, J. Howbert, 2014, Molecular & Cellular Proteomics.

Detecting cross-linked peptides by searching against a database of cross-linked peptide pairs.

William Stafford Noble, David R Goodlett, Sean McIlwain, 2010, Journal of proteome research.

A biologist ’ s introduction to support vector machines

William Stafford Noble, 2006 .

Joint precursor elution profile inference via regression for peptide detection in data-independent acquisition mass spectra

William Stafford Noble, Yang Young Lu, J. Bilmes, 2018, bioRxiv.

The essential transcriptional regulator HDP1 drives expansion of the inner membrane complex during early sexual differentiation of malaria parasites

William Stafford Noble, D. Patel, M. Llinás, 2020, bioRxiv.

A novel DNA-binding protein regulates sexual differentiation in malaria

William Stafford Noble, D. Patel, M. Llinás, 2020 .

ENCODE regions Identification of higher-order functional domains in the human data

William Stafford Noble, J. Stamatoyannopoulos, R. Thurman, 2007 .

Massively multiplex single-cell Hi-C

William Stafford Noble, J. Shendure, K. Gunderson, 2016, bioRxiv.

1 Support vector machine applications in computational biology

William Stafford Noble, 2022 .

Massively parallel profiling and predictive modeling of the outcomes of CRISPR/Cas9-mediated double-strand break repair

William Stafford Noble, Jacob M. Schreiber, A. McKenna, 2018, bioRxiv.

architecture and gene expression genomethe erythrocytic cycle reveals a strong connection between genome during P . falciparum Three-dimensional modeling of the Material Supplemental

William Stafford Noble, Jean-Philippe Vert, E. Bunnik, 2014 .

Measuring significant changes in chromatin conformation with ACCOST

William Stafford Noble, Borislav H. Hristov, Kate B. Cook, 2019, bioRxiv.

Inferring Diploid 3 D Chromatin Structures from HiC Data

William Stafford Noble, N. Varoquaux, A. Cauer, 2019 .

Dynamics of genome reorganization during human cardiogenesis reveal an RBM20-dependent splicing factory

William Stafford Noble, J. Shendure, V. Ramani, 2019, Nature Communications.

Matrix-matched calibration curves for assessing analytical figures of merit in quantitative proteomics

William Stafford Noble, Lindsay K. Pino, B. Searle, 2019, bioRxiv.

Visualization and Dissemination of Multidimensional Proteomics Data Comparing Protein Abundance During Caenorhabditis elegans Development

William Stafford Noble, Gennifer E. Merrihew, M. MacCoss, 2015, Journal of The American Society for Mass Spectrometry.

Linking cells across single-cell modalities by synergistic matching of neighborhood structure

William Stafford Noble, Borislav H. Hristov, J. Bilmes, 2022, bioRxiv.

Integrated systems biology analysis of KSHV latent infection reveals viral induction and reliance on peroxisome mediated lipid metabolism

William Stafford Noble, A. Gitter, Alejandro Wolf-Yadlin, 2017, PLoS pathogens.

Analysis of strain and regional variation in gene expression in mouse brain

William Stafford Noble, Paul Pavlidis, William S Noble, 2001, Genome Biology.

Training Compressed Fully-Connected Networks with a Density-Diversity Penalty

Jeff A. Bilmes, William Stafford Noble, Shengjie Wang, 2016, ICLR.

Detecting more peptides from bottom-up mass spectrometry data via peptide-level target-decoy competition

William Stafford Noble, U. Keich, A. Lin, 2022, bioRxiv.

Gut Microbial Protein Expression in Response to Dietary Patterns in a Controlled Feeding Study: A Metaproteomic Approach

William Stafford Noble, Damon H. May, M. Neuhouser, 2020, Microorganisms.

Sequence analysis DNA sequence 1 shape kernel enables alignment-free modeling of transcription factor binding

William Stafford Noble, R. Rohs, L. Yang, 2017 .

Analysis of secondary structure in proteins by chemical cross‐linking coupled to MS

William Stafford Noble, F. Gozzo, S. McIlwain, 2012, Proteomics.

DNA sequence 1 shape kernel enables alignment-free modeling of transcription factor binding

William Stafford Noble, Lin Yang, R. Rohs, 2017 .

Group using Constraint Graph Use Constraints to Guide Structure Learning

William Stafford Noble, Jacob M. Schreiber, 2017 .

Non-parametric estimation of posterior error probabilities associated with peptides identiﬁed by tandem mass spectrometry

William Stafford Noble, John D. Storey, L. Käll, 2008 .

The Crux toolkit for analysis of bottom-up tandem mass spectrometry proteomics data

William Stafford Noble, Yang Young Lu, Charles E. Grant, 2022, bioRxiv.

Learning Models of Biological Sequences

William Stafford Noble, C. Leslie, 2010, Encyclopedia of Machine Learning.

widespread , transient , and DNA-directed The spring-loaded genome : Nucleosome redistributions are Material Supplemental

William Stafford Noble, Daniel L. Vera, Justin A. Fincher, 2014 .

Primary and secondary auditory cortex stimulation for intractable tinnitus. Commentary

William Stafford Noble, A. Møller, D. Ridder, 2006 .

Predicting A/B compartments from histone modifications using deep learning

William Stafford Noble, M. Gerstein, Erez Lieberman-Aiden, 2022, bioRxiv.

Author Correction: Avocado: a multi-scale deep tensor factorization method learns a latent representation of the human epigenome

William Stafford Noble, Jacob M. Schreiber, Timothy J. Durham, 2021, Genome biology.

Escape from X Inactivation Varies in Mouse Tissues

William Stafford Noble, J. Shendure, C. Disteche, 2015, PLoS genetics.

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

William Stafford Noble, James C. Wright, Jacob M. Schreiber, 2022, Cell.

Matrix prior for data transfer between single cell data types in latent Dirichlet allocation

William Stafford Noble, Timothy J. Durham, L. Gevirtzman, 2022, bioRxiv.

Method Quantifying similarity between motifs

William Stafford Noble, J. Stamatoyannopoulos, T. Bailey, 2007 .

Improving peptide-level mass spectrometry analysis via double competition

William Stafford Noble, U. Keich, A. Lin, 2022, bioRxiv.

Fundamental Limits of Multi-Sample Flow Graph Decomposition

William Stafford Noble, Sreeram Kannan, I. Shomorony, 2022, 2022 IEEE International Symposium on Information Theory (ISIT).

The lncRNA Firre anchors the inactive X chromosome to the nucleolus by binding CTCF and maintains H3K27me3 methylation

William Stafford Noble, J. Shendure, C. Disteche, 2015, Genome Biology.

Mapping 3 D genome architecture through in situ DNase

William Stafford Noble, J. Shendure, R. Qiu, 2016 .

The MEME Suite

William Stafford Noble, Timothy L. Bailey, William S. Noble, 2015, Nucleic Acids Res..

Technical advances in proteomics: new developments in data-independent acquisition

William Stafford Noble, Alejandro Wolf-Yadlin, William S. Noble, 2016, F1000Research.

Regions Bound by 119 Human Transcription Factors Sequence Features and Chromatin Structure around the Genomic Material Supplemental

William Stafford Noble, Nathan C. Sheffield, Eurie L. Hong, 2012 .

Semi-supervised single-cell cross-modality translation using Polarbear

William Stafford Noble, Jean-Philippe Vert, Laetitia Meng-Papaxanthos, 2021, bioRxiv.

Trans- and cis-acting effects of Firre on epigenetic features of the inactive X chromosome

William Stafford Noble, J. Rinn, J. Shendure, 2020, Nature Communications.

Bipartite structure of the inactive mouse X chromosome

William Stafford Noble, Andrew J. Hill, J. Shendure, 2015, Genome Biology.

Systematic reconstruction of the cellular trajectories of mammalian embryogenesis

William Stafford Noble, J. Shendure, C. Disteche, 2021, bioRxiv.

Mammalian X upregulation is associated with enhanced transcription initiation, RNA half-life, and MOF-mediated H4K16 acetylation.

William Stafford Noble, Joseph B Hiatt, J. Shendure, 2013, Developmental cell.

Upregulation of the mammalian X chromosome is associated with enhanced transcription initiation , MOF-mediated H 4 K 16 acetylation , and longer RNA half-life

William Stafford Noble, J. Shendure, C. Disteche, 2013 .

Navigating the pitfalls of applying machine learning in genomics

William Stafford Noble, Jacob M. Schreiber, K. Pollard, 2021, Nature Reviews Genetics.

Independent and complementary methods for large-scale structural analysis of mammalian chromatin.

William Stafford Noble, Daniel G Peterson, William S Noble, 2007, Genome research.

The spring-loaded genome: Nucleosome redistributions are widespread, transient, and DNA-directed

William Stafford Noble, Eric Chicken, Daniel L. Vera, 2014, Genome research.

Multi-tissue integrative analysis of personal epigenomes

William Stafford Noble, James C. Wright, Jacob M. Schreiber, 2021 .

Genome-scale mapping of DNase I sensitivity in vivo using tiling DNA microarrays

William Stafford Noble, J. Stamatoyannopoulos, M. Hawrylycz, 2006, Nature Methods.

Widely distributed noncoding purifying selection in the human genome

William Stafford Noble, Charles E Grant, William S Noble, 2007, Proceedings of the National Academy of Sciences.

mokapot: Fast and Flexible Semisupervised Learning for Peptide Detection

William Stafford Noble, William E. Fondrie, 2021, Journal of proteome research.

High-Throughput Single-Cell Sequencing with Linear Amplification.

William Stafford Noble, Andrew C. Adey, J. Shendure, 2019, Molecular cell.

Segway : simultaneous segmentation of multiple functional genomics data sets with heterogeneous patterns of missing data

William Stafford Noble, Orion J. Buske, J. Bilmes, 2009 .

Implications of COMT long-range interactions on the phenotypic variability of 22q11.2 deletion syndrome

William Stafford Noble, Michael J. Zeitz, F. Ay, 2013, Nucleus.

Computational methods for analyzing and modeling genome structure and organization

William Stafford Noble, William S Noble, Galip Gürkan Yardımcı, 2018, Wiley interdisciplinary reviews. Systems biology and medicine.

Chromatin compartment dynamics in a haploinsufficient model of cardiac laminopathy

William Stafford Noble, J. Shendure, Deok‐Ho Kim, 2019, bioRxiv.

Dynamics of genome reorganization during human cardiogenesis reveal an RBM20-dependent splicing factory

William Stafford Noble, J. Shendure, V. Ramani, 2019, Nature communications.

Integrative detection and analysis of structural variation in cancer genomes

William Stafford Noble, Jesse R. Dixon, Royden A. Clark, 2018, Nature Genetics.

Analysis methods for studying the 3D architecture of the genome

William Stafford Noble, F. Ay, 2015, Genome Biology.

Trans- and cis-acting effects of the lncRNA Firre on epigenetic and structural features of the inactive X chromosome

William Stafford Noble, J. Rinn, J. Shendure, 2019, bioRxiv.

Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation

William Stafford Noble, Jun S. Song, Sushmita Roy, 2021, bioRxiv.

Genomic Interaction Profiles in Breast Cancer Reveal Altered Chromatin Architecture

William Stafford Noble, William S. Noble, Ferhat Ay, 2013, PloS one.

Epiphany: predicting Hi-C contact maps from 1D epigenomic signals

William Stafford Noble, Vianne R. Gao, J. Bilmes, 2021, bioRxiv.

The dynamic three-dimensional organization of the diploid yeast genome

William Stafford Noble, Jay Shendure, William S Noble, 2016, bioRxiv.

A High-Throughput Screen for Transcription Activation Domains Reveals Their Sequence Features and Permits Prediction by Deep Learning.

William Stafford Noble, Jacob M. Schreiber, J. Söding, 2020, Molecular cell.

How does multiple testing correction work?

William Stafford Noble, William S Noble, 2009, Nature Biotechnology.

Chromatin Fiber Folding Represses Transcription and Loop Extrusion in Quiescent Cells

William Stafford Noble, T. Schlick, Dejun Lin, 2020, bioRxiv.

Detecting more peptides from bottom-up mass spectrometry data via peptide-level target-decoy competition

William Stafford Noble, U. Keich, A. Lin, 2022, bioRxiv.

An averaging strategy to reduce variability in target-decoy estimates of false discovery rate

William Stafford Noble, Uri Keich, Kaipo Tamura, 2018, bioRxiv.

Response to “Mass spectrometrists should search for all peptides, but assess only the ones they care about”

William Stafford Noble, U. Keich, 2017, Nature Methods.

Title Comparative analysis of metazoan chromatin organization Permalink

William Stafford Noble, Stephen C. J. Parker, Maxwell W. Libbrecht, 2014 .

Using DNase Hi-C techniques to map global and local three-dimensional genome architecture at high resolution

William Stafford Noble, Ferhat Ay, Jay Shendure, 2017, bioRxiv.

Prospects & Overviews Multiple dimensions of epigenetic gene regulation in the malaria parasite Plasmodium falciparum

William Stafford Noble, Jean-Philippe Vert, E. Bunnik, 2014 .

ppx: Programmatic access to proteomics data repositories

William Stafford Noble, William E. Fondrie, W. Bittremieux, 2021, bioRxiv.

High-throughput mapping of meiotic crossover and chromosome mis-segregation events in interspecific hybrid mice

William Stafford Noble, Andrew C. Adey, J. Shendure, 2018, bioRxiv.

Massively parallel characterization of transcriptional regulatory elements in three diverse human cell types

William Stafford Noble, J. Shendure, Martin Kircher, 2023, bioRxiv.

Subzero, saline incubations of Colwellia psychrerythraea reveal strategies and biomarkers for sustained life in extreme icy environments.

William Stafford Noble, William E. Fondrie, B. Nunn, 2021, Environmental microbiology.

MoMo: Discovery of statistically significant post-translational modification motifs

William Stafford Noble, Alice Cheng, Charles E Grant, 2018, bioRxiv.

Cis-Compound Mutations are Prevalent in Triple Negative Breast Cancer and Can Drive Tumor Progression

William Stafford Noble, W. L. Ruzzo, D. Haussler, 2016, bioRxiv.

Choosing panels of genomics assays using submodular optimization

William Stafford Noble, Maxwell W. Libbrecht, J. Bilmes, 2016, bioRxiv.

Riboproteomics of the hepatitis C virus internal ribosomal entry site.

William Stafford Noble, D. Payan, Weiqun Li, 2004, Journal of proteome research.

Fine-mapping of nuclear compartments using ultra-deep Hi-C shows that active promoter and enhancer elements localize in the active A compartment even when adjacent sequences do not

William Stafford Noble, Erez Lieberman Aiden, M. Talkowski, 2021, bioRxiv.

X-Chromosome Inactivation and Escape from X Inactivation in Mouse.

William Stafford Noble, C. Disteche, G. Bonora, 2018, Methods in molecular biology.

Tigerfish designs oligonucleotide-based in situ hybridization probes targeting intervals of highly repetitive DNA at the scale of genomes

William Stafford Noble, Karen H. Miga, Conor K. Camplisson, 2023, bioRxiv.

Systematic proteomics of endogenous human cohesin reveals an interaction with diverse splicing factors and RNA-binding proteins required for mitotic progression

William Stafford Noble, B. Budnik, William Arbuthnot Sir Lane, 2019, The Journal of Biological Chemistry.

Local chromatin fiber folding represses transcription and loop extrusion in quiescent cells

William Stafford Noble, T. Schlick, Dejun Lin, 2021, eLife.

Distinct epigenetic features of differentiation-regulated replication origins

William Stafford Noble, Maxwell W. Libbrecht, M. Aladjem, 2016, Epigenetics & Chromatin.

A high-throughput screen for transcription activation domains reveals their sequence characteristics and permits reliable prediction by deep learning

William Stafford Noble, J. Söding, S. Hahn, 2019, bioRxiv.

Zero-shot imputations across species are enabled through joint modeling of human and mouse epigenomics

William Stafford Noble, Jacob M. Schreiber, Jacob Schreiber, 2019, bioRxiv.

Identification of genes escaping X inactivation by allelic expression analysis in a novel hybrid mouse model

William Stafford Noble, J. Shendure, C. Disteche, 2015, Data in brief.

Class ificat ion of Clear-Cel l Sarcoma as a Subtype of Melanoma by Genomic Profil ing

William Stafford Noble, A. Viale, C. Antonescu, 2003 .

Sequencing Information for Each Library

William Stafford Noble, J. Shendure, S. Fields, 2022 .

Efficient indexing of peptides for database search using Tide

William Stafford Noble, Attila Kertész-Farkas, Frank Lawrence Nii Adoquaye Acquaye, 2022, bioRxiv.

Metaproteomics reveal that rapid perturbations in organic matter prioritize functional restructuring over taxonomy in western Arctic Ocean microbiomes

William Stafford Noble, William S Noble, Damon H May, 2019, The ISME Journal.

Critical decisions in metaproteomics: achieving high confidence protein annotations in a sea of unknowns

William Stafford Noble, William S Noble, Damon H May, 2016, The ISME Journal.

Dynamic Bayesian Network for Accurate Detection of Peptides from Tandem Mass Spectra.

William Stafford Noble, William S Noble, John T. Halloran, 2016, Journal of proteome research.

An Alignment-Free "Metapeptide" Strategy for Metaproteomic Characterization of Microbiome Samples Using Shotgun Metagenomic Sequencing.

William Stafford Noble, Elhanan Borenstein, William S Noble, 2016, Journal of proteome research.

Faster graphical model identification of tandem mass spectra using peptide word lattices

Jeff A. Bilmes, William Stafford Noble, Shengjie Wang, 2014, 1410.7875.

Multiple functional categories of proteins identified in an in vitro cellular ubiquitin affinity extract using shotgun peptide sequencing.

William Stafford Noble, D. Payan, Weiqun Li, 2003, Journal of proteome research.

Cohesin interacts with a panoply of splicing factors required for cell cycle progression and genomic organization

William Stafford Noble, B. Budnik, William Arbuthnot Sir Lane, 2018, bioRxiv.

A review of statistical methods for protein identification using tandem mass spectrometry.

William Stafford Noble, William Noble, Oliver Serang, 2012, Statistics and its interface.

Integrative annotation of chromatin elements from ENCODE data

William Stafford Noble, Maxwell W. Libbrecht, Robert S. Harris, 2012, Nucleic Acids Research.

A statistical approach to peptide identification from clustered tandem mass spectrometry data

William Stafford Noble, Vladimir N. Minin, Soyoung Ryu, 2012, 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops.

HiC-spector: a matrix library for spectral and reproducibility analysis of Hi-C contact maps

Mark Gerstein, William Stafford Noble, Chengfei Yan, 2016, bioRxiv.

A pitfall for machine learning methods aiming to predict across cell types

William Stafford Noble, Jacob M. Schreiber, Ritambhara Singh, 2020, Genome Biology.

Inference of genome 3D architecture by modeling overdispersion of Hi-C data

William Stafford Noble, Jean-Philippe Vert, N. Varoquaux, 2021, bioRxiv.

The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

William Stafford Noble, Mark J. Carman, Yun S. Song, 2022, bioRxiv.

Software tools for visualizing Hi-C data

William Stafford Noble, G. Yardımcı, 2016, bioRxiv.

Large-scale tandem mass spectrum clustering using fast nearest neighbor searching

William Stafford Noble, P. Dorrestein, W. Bittremieux, 2021, bioRxiv.

Support vector machines (SVMs) are becoming popular in a wide variety of biological applications. But, what exactly are SVMs and how do they work? And what are their most promising applications in the life sciences?

William Stafford Noble, William S Noble, 2006 .

DIAmeter: matching peptides to data-independent acquisition mass spectrometry data

William Stafford Noble, Yang Young Lu, J. Bilmes, 2021, bioRxiv.

Fibertools: fast and accurate DNA-m6A calling using single-molecule long-read sequencing

William Stafford Noble, Mitchell R. Vollger, A. Stergachis, 2023 .

MultiomicsTracks96: A high throughput PIXUL-Matrix-based toolbox to profile frozen and FFPE tissues multiomes

William Stafford Noble, T. Vaisar, K. Bomsztyk, 2023, bioRxiv.

A single-cell transcriptional timelapse of mouse embryonic development, from gastrula to pup

William Stafford Noble, Beth K. Martin, J. Shendure, 2023, bioRxiv.

Reducing peptide sequence bias in quantitative mass spectrometry data with machine learning

William Stafford Noble, Devin K. Schweppe, Sewoong Oh, 2022, bioRxiv.

Evaluating proteomics imputation methods with improved criteria

William Stafford Noble, William E. Fondrie, Sewoong Oh, 2023, bioRxiv.

Detecting modifications in proteomics experiments with Param-Medic

William Stafford Noble, Kaipo Tamura, William S Noble, 2018, bioRxiv.

PREDICTD PaRallel Epigenomics Data Imputation with Cloud-based Tensor Decomposition

William Stafford Noble, Maxwell W. Libbrecht, Timothy J. Durham, 2018, Nature Communications.

Robust Decoy-enhanced Saliency Maps.

William Stafford Noble, Xinyu Xing, Yang Lu, 2020 .

Multimodal Single-Cell Translation and Alignment with Semi-Supervised Learning

William Stafford Noble, Jean-Philippe Vert, Laetitia Meng-Papaxanthos, 2022, J. Comput. Biol..

Improving power while controlling the false discovery rate when only a subset of peptides are relevant

William Stafford Noble, Uri Keich, Andy Lin, 2020, bioRxiv.

Competition-based control of the false discovery proportion.

William Stafford Noble, U. Keich, Kristen Emery, 2020, Biometrics.

Multiple competition-based FDR control for peptide detection.

William Stafford Noble, U. Keich, Kristen Emery, 2019, 1907.01458.

ISMB 2014 Proceedings Papers Committee

William Stafford Noble, Noah M. Daniels, D. Heckerman, 2014, Bioinformatics.

Chromatin alternates between A and B compartments at kilobase scale for subgenic organization

William Stafford Noble, Erez Lieberman Aiden, A. Omer, 2023, Nature communications.

Comprehensive statistical inference of the clonal structure of cancer from multiple biopsies

William Stafford Noble, Jie Liu, Jay Shendure, 2017, Scientific Reports.

Calibration Using a Single-Point External Reference Material Harmonizes Quantitative Mass Spectrometry Proteomics Data between Platforms and Laboratories.

William Stafford Noble, Lindsay K. Pino, B. Searle, 2018, Analytical chemistry.

Nucleosome positioning signals in genomic DNA data

William Stafford Noble, Z. Weng, J. Stamatoyannopoulos, 2007 .

Spatial and temporal organization of the genome: Current state and future aims of the 4D nucleome project.

William Stafford Noble, Jennifer E. Phillips-Cremins, Brian J. Beliveau, 2023, Molecular cell.

Interpretation of the DOME Recommendations for Machine Learning in Proteomics and Metabolomics

William Stafford Noble, S. Degroeve, Mathias Wilhelm, 2022, Journal of proteome research.

Edison: measuring scaffolding accuracy with edit distance

William Stafford Noble, P. Myler, Shawn Sullivan, 2022, bioRxiv.

ChromaFold predicts the 3D contact map from single-cell chromatin accessibility

William Stafford Noble, Vianne R. Gao, J. Bilmes, 2023, bioRxiv.

Epiphany: Predicting the Hi-C Contact Map from 1D Epigenomic Data

William Stafford Noble, Vianne R. Gao, J. Bilmes, 2021 .

Sequence-to-sequence translation from mass spectra to peptides with a transformer model

William Stafford Noble, William E. Fondrie, Sewoong Oh, 2023, bioRxiv.

De novo mass spectrometry peptide sequencing with a transformer model

William Stafford Noble, William E. Fondrie, Sewoong Oh, 2022, bioRxiv.

A flexible workflow for building spectral libraries from narrow window data independent acquisition mass spectrometry data

William Stafford Noble, Alexander J. Federation, William E. Fondrie, 2021, bioRxiv.

Mismatch string kernels for discriminative protein classification

William Stafford Noble, J. Weston, E. Eskin, 2004, Bioinform..

Transcription

William Stafford Noble, C. M. Sperberg-McQueen, Claus Huitfeldt, 2005, Chemistry and Biology of Non‐Canonical Nucleic Acids.

Human Transcription Factors Running title : Analysis of Sites of 119 TFs in the Human Genome

William Stafford Noble, Melissa C. Greven, E. Birney, 2012 .

CTCF-mediated insulation and chromatin environment modulate Car5b escape from X inactivation

William Stafford Noble, J. Shendure, S. Henikoff, 2023, bioRxiv.

Support vector machine classification on the web

William Stafford Noble, P. Pavlidis, I. Wapinski, 2004, Bioinform..

Novel insights into the role of long non-coding RNA in the human malaria parasite, Plasmodium falciparum

William Stafford Noble, Borislav H. Hristov, K. L. Le Roch, 2023, Nature communications.

LSMMD-MA: scaling multimodal data integration for single-cell genomics data analysis

William Stafford Noble, Marco Cuturi, Jean-Philippe Vert, 2022, bioRxiv.

Joint precursor elution profile inference via regression for peptide detection in data-independent acquisition mass spectra

William Stafford Noble, Yang Young Lu, J. Bilmes, 2018, bioRxiv.

Bipartite matching generalizations for peptide identification in tandem mass spectrometry

William Stafford Noble, Wenruo Bai, William S. Noble, 2016, BCB.

Bridging the False Discovery Gap.

William Stafford Noble, U. Keich, Jack Freestone, 2023, Journal of proteome research.

Bounding the FDP in competition-based control of the FDR

William Stafford Noble, U. Keich, Jack Freestone, 2023, 2302.11837.

Unsupervised segmentation of continuous genomic data

William Stafford Noble, J. Stamatoyannopoulos, B. Schmidt, 2007, Bioinform..

Improving Peptide-Level Mass Spectrometry Analysis via Double Competition.

William Stafford Noble, U. Keich, A. Lin, 2022, Journal of Proteome Research.

Analysis of tandem mass spectrometry data with CONGA: Combining Open and Narrow searches with Group-wise Analysis

William Stafford Noble, U. Keich, Jack Freestone, 2023, bioRxiv.

Re-investigating the correctness of decoy-based false discovery rate control in proteomics tandem mass spectrometry

William Stafford Noble, U. Keich, Jack Freestone, 2023, bioRxiv.

Target-decoy false discovery rate estimation using Crema

William Stafford Noble, William E. Fondrie, U. Keich, 2023, bioRxiv.

Group-walk, a rigorous approach to group-wise false discovery rate analysis by target-decoy competition

William Stafford Noble, U. Keich, Temana Short, 2022, bioRxiv.

Resource A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens Graphical

William Stafford Noble, Andrew J. Hill, Beth K. Martin, 2019 .

On the feasibility and utility of exploiting real time database search to improve adaptive peak selection

William Stafford Noble, M. MacCoss, Benjamin Diament, 2012, 1207.5848.

Inference of 3D genome architecture by modeling overdispersion of Hi-C data

William Stafford Noble, Jean-Philippe Vert, N. Varoquaux, 2023, Bioinform..

Identifying multi-locus chromatin contacts in human cells using tethered multiple 3C

William Stafford Noble, Michael J. Zeitz, Jean-Philippe Vert, 2015, BMC Genomics.

Machine Learning in Computational Biology

William Stafford Noble, C. Leslie, G. Rätsch, 2007, NIPS 2007.

Bioinformatics Original Paper Support Vector Machine Learning from Heterogeneous Data: an Empirical Analysis Using Protein Sequence and Structure

William Stafford Noble, Darrin P. Lewis, Tony Jebara, 2022 .

Unsupervised pattern discovery in human chromatin structure through genomic segmentation

William Stafford Noble, Orion J. Buske, J. Bilmes, 2012, Nature Methods.

Tinnitus and Hyperacusis

William Stafford Noble, A. Bardia, R. Tyler, 2009 .

The MEME Suite

William Stafford Noble, Charles E. Grant, T. Bailey, 2015, Nucleic Acids Res..

Self‐Reports about Tinnitus and about Cochlear Implants

William Stafford Noble, W. Noble, 2000, Ear and hearing.

SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

William Stafford Noble, J. Weston, C. Leslie, 2007, BMC Bioinformatics.

Faster and more accurate graphical model identification of tandem mass spectra using trellises

Jeff A. Bilmes, William Stafford Noble, Shengjie Wang, 2016, Bioinform..

Support vector machine

William Stafford Noble, 2013 .

Using machine learning to speed up manual image annotation: application to a 3D imaging protocol for measuring single cell gene expression in the developing C. elegans embryo

William Stafford Noble, R. Waterston, J. Murray, 2010, BMC Bioinformatics.

Epigenetic priors for identifying active transcription factor binding sites

William Stafford Noble, Fabian A. Buske, T. Bailey, 2012, Bioinform..

Exploratory analysis of genomic segmentations with Segtools

William Stafford Noble, Orion J. Buske, M. M. Hoffman, 2011, BMC Bioinformatics.

Comprehensive characterization of tissue-specific chromatin accessibility in L2 Caenorhabditis elegans nematodes

William Stafford Noble, Timothy J. Durham, J. Shendure, 2021, Genome research.