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ORCIDWilliam Stafford Noble
William S. Noble – William Noble Grundy
2020 – today
- 2024
[j95]Varun Ananth, Justin Sanders, Melih Yilmaz, Bo Wen
, Sewoong Oh, William Stafford Noble:
A learned score function improves the power of mass spectrometry database search. Bioinform. 40(Supplement_1): i410-i417 (2024)- [j94]Tangqi Fang, Yifeng Liu, Addie Woicik, Minsi Lu, Anupama Jha, Xiao Wang, Gang Li, Borislav H. Hristov, Zixuan Liu, Hanwen Xu, William S. Noble, Sheng Wang:
Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model. Bioinform. 40(Supplement_1): i471-i480 (2024) - [c63]Jack Freestone, Lukas Käll, William Stafford Noble, Uri Keich:
Semi-supervised Learning While Controlling the FDR with an Application to Tandem Mass Spectrometry Analysis. RECOMB 2024: 448-453 - [i8]Winston Chen, Yifan Jiang, William Stafford Noble, Yang Young Lu:
Error-controlled non-additive interaction discovery in machine learning models. CoRR abs/2408.17016 (2024) - 2023
- [j93]Nelle Varoquaux, William S. Noble, Jean-Philippe Vert:
Inference of 3D genome architecture by modeling overdispersion of Hi-C data. Bioinform. 39(1) (2023) - [j92]Andy Lin, Brooke L. Deatherage Kaiser, Janine R. Hutchison, Jeffrey A. Bilmes, William Stafford Noble:
MS1Connect: a mass spectrometry run similarity measure. Bioinform. 39(2) (2023) - [j91]William Stafford Noble:
Ten simple rules for defining a computational biology project. PLoS Comput. Biol. 19(1) (2023) - [j90]Alan Min, Timothy Durham, Louis Gevirtzman, William Stafford Noble:
Matrix prior for data transfer between single cell data types in latent Dirichlet allocation. PLoS Comput. Biol. 19(5) (2023) - [j89]Brittany Baur, Junha Shin, Jacob Schreiber, Shilu Zhang, Yi Zhang, Mohith Manjunath, Jun S. Song, William Stafford Noble, Sushmita Roy:
Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation. PLoS Comput. Biol. 19(7) (2023) - [i7]Winston Chen, William Stafford Noble, Yang Young Lu:
DeepROCK: Error-controlled interaction detection in deep neural networks. CoRR abs/2309.15319 (2023) - 2022
- [j88]Borislav H. Hristov, Jeff A. Bilmes, William Stafford Noble:
Linking cells across single-cell modalities by synergistic matching of neighborhood structure. Bioinform. 38(Supplement_2): ii148-ii154 (2022) - [j87]Jack Freestone, Temana Short, William Stafford Noble, Uri Keich:
Group-walk: a rigorous approach to group-wise false discovery rate analysis by target-decoy competition. Bioinform. 38(Supplement_2): ii82-ii88 (2022) - [j86]Pinar Demetci, Rebecca Santorella, Björn Sandstede, William Stafford Noble, Ritambhara Singh:
SCOT: Single-Cell Multi-Omics Alignment with Optimal Transport. J. Comput. Biol. 29(1): 3-18 (2022) - [j85]Pinar Demetci, Rebecca Santorella, Björn Sandstede, William Stafford Noble, Ritambhara Singh:
Single-Cell Multiomics Integration by SCOT. J. Comput. Biol. 29(1): 19-22 (2022) - [j84]Ran Zhang, Laetitia Meng-Papaxanthos, Jean-Philippe Vert, William Stafford Noble:
Multimodal Single-Cell Translation and Alignment with Semi-Supervised Learning. J. Comput. Biol. 29(11): 1198-1212 (2022) - [c62]Melih Yilmaz, William Fondrie, Wout Bittremieux, Sewoong Oh, William S. Noble:
De novo mass spectrometry peptide sequencing with a transformer model. ICML 2022: 25514-25522 - [c61]Kayvon Mazooji, Sreeram Kannan, William Stafford Noble, Ilan Shomorony:
Fundamental Limits of Multi-Sample Flow Graph Decomposition. ISIT 2022: 2403-2408 - [c60]Ran Zhang, Laetitia Meng-Papaxanthos, Jean-Philippe Vert, William Stafford Noble:
Semi-supervised Single-Cell Cross-modality Translation Using Polarbear. RECOMB 2022: 20-35 - [d3]Laetitia Meng-Papaxanthos, Ran Zhang, Gang Li, Marco Cuturi, William Stafford Noble, Jean-Philippe Vert:
Software for Large Scale MMD-MA. Zenodo, 2022
- 2021
- [j83]Jacob M. Schreiber, Jeffrey A. Bilmes, William Stafford Noble:
Prioritizing transcriptomic and epigenomic experiments using an optimization strategy that leverages imputed data. Bioinform. 37(4): 439-447 (2021) - [j82]Dejun Lin, Justin Sanders, William Stafford Noble:
HiCRep.py: fast comparison of Hi-C contact matrices in Python. Bioinform. 37(18): 2996-2997 (2021) - [j81]Yang Young Lu, Jeff A. Bilmes, Ricard A. Rodriguez-Mias, Judit Villén, William Stafford Noble:
DIAmeter: matching peptides to data-independent acquisition mass spectrometry data. Bioinform. 37(Supplement): 434-432 (2021) - [c59]Yang Young Lu, Wenbo Guo, Xinyu Xing, William Stafford Noble:
DANCE: Enhancing saliency maps using decoys. ICML 2021: 7124-7133 - [c58]Yang Young Lu, Timothy C. Yu, Giancarlo Bonora, William Stafford Noble:
ACE: Explaining cluster from an adversarial perspective. ICML 2021: 7156-7167 - 2020
- [j80]Jacob M. Schreiber, Jeffrey A. Bilmes, William Stafford Noble:
apricot: Submodular selection for data summarization in Python. J. Mach. Learn. Res. 21: 161:1-161:6 (2020) - [j79]Hyeon-Jin Kim, Galip Gürkan Yardimci, Giancarlo Bonora, Vijay Ramani, Jie Liu, Ruolan Qiu, Choli Lee, Jennifer Hesson, Carol B. Ware, Jay Shendure, Zhi-jun Duan, William Stafford Noble:
Capturing cell type-specific chromatin compartment patterns by applying topic modeling to single-cell Hi-C data. PLoS Comput. Biol. 16(9) (2020) - [c57]Jacob M. Schreiber, Timothy J. Durham, William S. Noble, Jeffrey A. Bilmes:
Avocado: Deep tensor factorization characterizes the human epigenome via imputation of tens of thousands of functional experiments. BCB 2020: 37:1 - [c56]Jacob M. Schreiber, Deepthi Hegde, William S. Noble:
Zero-shot imputations across species are enabled through joint modeling of human and mouse epigenomics. BCB 2020: 39:1-39:9 - [c55]Ritambhara Singh, Pinar Demetci, Giancarlo Bonora, Vijay Ramani, Choli Lee, He Fang, Zhi-jun Duan, Xinxian Deng, Jay Shendure, Christine Disteche, William Stafford Noble:
Unsupervised manifold alignment for single-cell multi-omics data. BCB 2020: 40:1-40:10 - [c54]Wei Yang, Jeffrey A. Bilmes, William Stafford Noble:
Submodular sketches of single-cell RNA-seq measurements. BCB 2020: 61:1-61:6 - [c53]Kristen Emery, Syamand Hasam, William Stafford Noble, Uri Keich:
Multiple Competition-Based FDR Control and Its Application to Peptide Detection. RECOMB 2020: 54-71 - [i6]Yang Lu, Wenbo Guo, Xinyu Xing, William Stafford Noble:
Robust saliency maps with decoy-enhanced saliency score. CoRR abs/2002.00526 (2020)
2010 – 2019
- 2019
- [j78]Musa Nur Gabere, William Stafford Noble:
Response to comments on 'Empirical comparison of web-based antimicrobial peptide prediction tools'. Bioinform. 35(15): 2695-2696 (2019) - [j77]Alice Cheng, Charles E. Grant, William S. Noble, Timothy L. Bailey:
MoMo: discovery of statistically significant post-translational modification motifs. Bioinform. 35(16): 2774-2782 (2019) - [j76]Michael Mommert, Michael S. P. Kelley, Miguel de Val-Borro, Jian-Yang Li, Giannina Guzman, Brigitta M. Sipocz, Josef Durech, Mikael Granvik, William S. Noble, Nick Moskovitz, Antti Penttilä, Nalin Samarasinha:
sbpy: A Python module for small-body planetary astronomy. J. Open Source Softw. 4(38): 1426 (2019) - [j75]David F. Read, Kate Cook, Yang Young Lu, Karine G. Le Roch, William Stafford Noble:
Predicting gene expression in the human malaria parasite Plasmodium falciparum using histone modification, nucleosome positioning, and 3D localization features. PLoS Comput. Biol. 15(9) (2019) - [j74]Wenruo Bai, Jeffrey A. Bilmes, William S. Noble:
Submodular Generalized Matching for Peptide Identification in Tandem Mass Spectrometry. IEEE ACM Trans. Comput. Biol. Bioinform. 16(4): 1168-1181 (2019) - [c52]Jie Liu, Yuanhao Huang, Ritambhara Singh, Jean-Philippe Vert, William Stafford Noble:
Jointly Embedding Multiple Single-Cell Omics Measurements. WABI 2019: 10:1-10:13 - [c51]Alexandra Gesine Cauer, Gürkan Yardimci, Jean-Philippe Vert, Nelle Varoquaux, William Stafford Noble:
Inferring Diploid 3D Chromatin Structures from Hi-C Data. WABI 2019: 11:1-11:13 - [i5]Jacob M. Schreiber, Jeffrey A. Bilmes, William Stafford Noble:
apricot: Submodular selection for data summarization in Python. CoRR abs/1906.03543 (2019) - 2018
- [j73]Rachel C. W. Chan, Maxwell W. Libbrecht, Eric G. Roberts, Jeffrey A. Bilmes, William Stafford Noble, Michael M. Hoffman:
Segway 2.0: Gaussian mixture models and minibatch training. Bioinform. 34(4): 669-671 (2018) - [j72]Jie Liu, Dejun Lin, Galip Gürkan Yardimci, William Stafford Noble:
Unsupervised embedding of single-cell Hi-C data. Bioinform. 34(13): i96-i104 (2018) - [j71]Oana Ursu, Nathan Boley, Maryna Taranova, Y. X. Rachel Wang, Galip Gürkan Yardimci, William Stafford Noble, Anshul Kundaje:
GenomeDISCO: a concordance score for chromosome conformation capture experiments using random walks on contact map graphs. Bioinform. 34(16): 2701-2707 (2018) - [c50]Aakash Sur, Jackie McDonald, Bryan Jensen, William S. Noble, Peter J. Myler:
Unveiling Elements of Genomic Architecture with Genome Wide Chromatin Conformation Capture. AMIA 2018 - [c49]Maxwell W. Libbrecht, Jeffrey A. Bilmes, William Stafford Noble:
Choosing Non-redundant Representative Subsets Of Protein Sequence Data Sets Using Submodular Optimization. BCB 2018: 566 - [c48]Wenruo Bai, William Stafford Noble, Jeff A. Bilmes:
Submodular Maximization via Gradient Ascent: The Case of Deep Submodular Functions. NeurIPS 2018: 7989-7999 - [c47]Yang Young Lu, Yingying Fan, Jinchi Lv, William Stafford Noble:
DeepPINK: reproducible feature selection in deep neural networks. NeurIPS 2018: 8690-8700 - [i4]Yang Young Lu, Yingying Fan, Jinchi Lv, William Stafford Noble:
DeepPINK: reproducible feature selection in deep neural networks. CoRR abs/1809.01185 (2018) - 2017
- [j70]Musa Nur Gabere, William Stafford Noble:
Empirical comparison of web-based antimicrobial peptide prediction tools. Bioinform. 33(13): 1921-1929 (2017) - [j69]Koon-Kiu Yan, Galip Gürkan Yardimci, Chengfei Yan, William S. Noble, Mark Gerstein:
HiC-spector: a matrix library for spectral and reproducibility analysis of Hi-C contact maps. Bioinform. 33(14): 2199-2201 (2017) - [j68]Wenxiu Ma, Lin Yang, Remo Rohs, William Stafford Noble:
DNA sequence+shape kernel enables alignment-free modeling of transcription factor binding. Bioinform. 33(19): 3003-3010 (2017) - [j67]Jacob M. Schreiber, William S. Noble:
Finding the optimal Bayesian network given a constraint graph. PeerJ Comput. Sci. 3: e122 (2017) - [j66]William Stafford Noble:
Ten simple rules for writing a response to reviewers. PLoS Comput. Biol. 13(10) (2017) - [c46]Shengjie Wang, Haoran Cai, Jeff A. Bilmes, William S. Noble:
Training Compressed Fully-Connected Networks with a Density-Diversity Penalty. ICLR (Poster) 2017 - [c45]Uri Keich, William Stafford Noble:
Progressive Calibration and Averaging for Tandem Mass Spectrometry Statistical Confidence Estimation: Why Settle for a Single Decoy? RECOMB 2017: 99-116 - [r2]William Stafford Noble, Christina S. Leslie:
Learning Models of Biological Sequences. Encyclopedia of Machine Learning and Data Mining 2017: 723-729 - [d2]Rachel C. W. Chan, Maxwell W. Libbrecht, Eric G. Roberts, Jeffrey A. Bilmes, William Stafford Noble, Michael M. Hoffman:
Segway 2.0 Application Note Datasets. Zenodo, 2017 - [d1]Rachel C. W. Chan, Maxwell W. Libbrecht, Eric G. Roberts, Jeffrey A. Bilmes, William Stafford Noble, Michael M. Hoffman:
Segway 2.0 Application Note Scripts. Zenodo, 2017 - [i3]Jacob M. Schreiber, William S. Noble:
Finding the optimal bayesian network given a constraint graph. PeerJ Prepr. 5: e2872 (2017) - 2016
- [j65]Charles E. Grant, James Johnson, Timothy L. Bailey, William Stafford Noble:
MCAST: scanning for cis-regulatory motif clusters. Bioinform. 32(8): 1217-1219 (2016) - [j64]Shengjie Wang, John T. Halloran, Jeff A. Bilmes, William S. Noble:
Faster and more accurate graphical model identification of tandem mass spectra using trellises. Bioinform. 32(12): 322-331 (2016) - [c44]Wenruo Bai, Jeffrey A. Bilmes, William S. Noble:
Bipartite matching generalizations for peptide identification in tandem mass spectrometry. BCB 2016: 327-336 - 2015
- [j63]Timothy L. Bailey, James Johnson, Charles E. Grant, William S. Noble:
The MEME Suite. Nucleic Acids Res. 43(Webserver-Issue): W39-W49 (2015) - [j62]Galip Gürkan Yardimci, William Stafford Noble:
Predictive model of 3D domain formation via CTCF-mediated extrusion. Proc. Natl. Acad. Sci. USA 112(47): 14404-14405 (2015) - [c43]Zafer Aydin, David Baker, William Stafford Noble:
Constructing Structural Profiles for Protein Torsion Angle Prediction. BIOINFORMATICS 2015: 26-35 - [c42]Zafer Aydin, David Baker, William Stafford Noble:
Template Scoring Methods for Protein Torsion Angle Prediction. BIOSTEC (Selected Papers) 2015: 206-223 - [c41]Maxwell W. Libbrecht, Michael M. Hoffman, Jeff A. Bilmes, William Stafford Noble:
Entropic Graph-based Posterior Regularization. ICML 2015: 1992-2001 - 2014
- [j61]Nelle Varoquaux, Ferhat Ay, William Stafford Noble, Jean-Philippe Vert:
A statistical approach for inferring the 3D structure of the genome. Bioinform. 30(12): 26-33 (2014) - [j60]Joshua Wing Kei Ho, Youngsook L. Jung, Tao Liu, Burak Han Alver, Soohyun Lee, Kohta Ikegami, Kyung-Ah Sohn, Aki Minoda, Michael Y. Tolstorukov, Alex Appert, Stephen C. J. Parker, Tingting Gu, Anshul Kundaje, Nicole C. Riddle, Eric Bishop, Thea A. Egelhofer, Sheng'en Shawn Hu, Artyom A. Alekseyenko, Andreas Rechtsteiner, Dalal Asker, Jason A. Belsky, Sarah K. Bowman, Q. Brent Chen, Ron A.-J. Chen, Daniel S. Day, Yan Dong, Andrea C. Dose, Xikun Duan, Charles B. Epstein, Sevinc Ercan, Elise A. Feingold, Francesco Ferrari, Jacob M. Garrigues, Nils Gehlenborg, Peter J. Good, Psalm Haseley, Daniel He, Moritz Herrmann, Michael M. Hoffman, Tess E. Jeffers, Peter V. Kharchenko, Paulina Kolasinska-Zwierz, Chitra V. Kotwaliwale, Nischay Kumar, Sasha A. Langley, Erica Larschan, Isabel Latorre, Maxwell W. Libbrecht, Xueqiu Lin, Richard Park, Michael J. Pazin, Hoang N. Pham, Annette Plachetka, Bo Qin, Yuri B. Schwartz, Noam Shoresh, Przemyslaw Stempor, Anne Vielle, Chengyang Wang, Christina M. Whittle, Huiling Xue, Robert E. Kingston, Ju Han Kim, Bradley E. Bernstein, Abby F. Dernburg, Vincenzo Pirrotta, Mitzi I. Kuroda, William S. Noble, Thomas D. Tullius, Manolis Kellis, David M. MacAlpine, Susan Strome, Sarah C. R. Elgin, Xiaole Shirley Liu, Jason D. Lieb, Julie Ahringer, Gary H. Karpen, Peter J. Park:
Comparative analysis of metazoan chromatin organization Open. Nat. 512(7515): 449-452 (2014) - [j59]Habil Zare, Junfeng Wang, Alex Hu, Kris Weber, Joshua D. Smith, Deborah A. Nickerson, ChaoZhong Song, Daniela M. Witten, C. Anthony Blau, William Stafford Noble:
Inferring Clonal Composition from Multiple Sections of a Breast Cancer. PLoS Comput. Biol. 10(7) (2014) - [c40]John T. Halloran, Jeff A. Bilmes, William Stafford Noble:
Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry. UAI 2014: 320-329 - 2013
- [c39]Michael M. Hoffman, Orion J. Buske, Jie Wang, Zhiping Weng, Jeff A. Bilmes, William Stafford Noble:
Unsupervised pattern discovery in human chromatin structure through genomic segmentation. BCB 2013: 813 - [c38]Alexander J. Hartemink, Manolis Kellis, William Stafford Noble, Zhiping Weng:
Session Introduction. Pacific Symposium on Biocomputing 2013: 65-68 - 2012
- [j58]Gabriel Cuéllar-Partida, Fabian A. Buske, Robert C. McLeay, Tom Whitington, William Stafford Noble, Timothy L. Bailey:
Epigenetic priors for identifying active transcription factor binding sites. Bioinform. 28(1): 56-62 (2012) - [j57]Sean McIlwain, Michael Mathews, Michael Bereman, Edwin W. Rubel, Michael J. MacCoss, William Stafford Noble:
Estimating relative abundances of proteins from shotgun proteomics data. BMC Bioinform. 13: 308 (2012) - [j56]Viktor Granholm, William Stafford Noble, Lukas Käll:
A cross-validation scheme for machine learning algorithms in shotgun proteomics. BMC Bioinform. 13(S-16): S3 (2012) - [j55]William Stafford Noble, Michael J. MacCoss:
Computational and Statistical Analysis of Protein Mass Spectrometry Data. PLoS Comput. Biol. 8(1) (2012) - [j54]Oliver Serang, William Stafford Noble:
Faster Mass Spectrometry-Based Protein Inference: Junction Trees Are More Efficient than Sampling and Marginalization by Enumeration. IEEE ACM Trans. Comput. Biol. Bioinform. 9(3): 809-817 (2012) - [c37]Soyoung Ryu, David R. Goodlett, William S. Noble, Vladimir N. Minin:
A statistical approach to peptide identification from clustered tandem mass spectrometry data. BIBM Workshops 2012: 648-653 - [c36]William Stafford Noble, C. Anthony Blau, Job Dekker, Zhi-jun Duan, Yi Mao:
The Structure and Function of Chromatin and Chromosomes. Pacific Symposium on Biocomputing 2012: 434-440 - [c35]Ajit P. Singh, John T. Halloran, Jeff A. Bilmes, Katrin Kirchhoff, William Stafford Noble:
Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra. UAI 2012: 775-785 - [i2]Ajit P. Singh, John T. Halloran, Jeff A. Bilmes, Katrin Kirchhoff, William Stafford Noble:
Spectrum Identification using a Dynamic Bayesian Network Model of Tandem Mass Spectra. CoRR abs/1210.4904 (2012) - 2011
- [j53]Charles E. Grant, Timothy L. Bailey, William Stafford Noble:
FIMO: scanning for occurrences of a given motif. Bioinform. 27(7): 1017-1018 (2011) - [j52]Emi Tanaka, Timothy L. Bailey, Charles E. Grant, William Stafford Noble, Uri Keich:
Improved similarity scores for comparing motifs. Bioinform. 27(12): 1603-1609 (2011) - [j51]Zafer Aydin, Ajit P. Singh, Jeff A. Bilmes, William Stafford Noble:
Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure. BMC Bioinform. 12: 154 (2011) - [j50]Orion J. Buske, Michael M. Hoffman, Nadia Ponts, Karine G. Le Roch, William Stafford Noble:
Exploratory analysis of genomic segmentations with Segtools. BMC Bioinform. 12: 415 (2011) - [j49]Iain Melvin, Jason Weston, William Stafford Noble, Christina S. Leslie:
Detecting Remote Evolutionary Relationships among Proteins by Large-Scale Semantic Embedding. PLoS Comput. Biol. 7(1) (2011) - [c34]William Stafford Noble, Zhi-jun Duan, Mirela Andronescu, Kevin Schutz, Sean McIlwain, Yoo Jung Kim, Choli Lee, Jay Shendure, Stanley Fields, C. Anthony Blau:
A Three-Dimensional Model of the Yeast Genome. RECOMB 2011: 320 - [p3]Yanjun Qi, William Stafford Noble:
Protein Interaction Networks: Protein Domain Interaction and Protein Function Prediction. Handbook of Statistical Bioinformatics 2011: 427-459 - 2010
- [j48]Michael M. Hoffman, Orion J. Buske, William Stafford Noble:
The Genomedata format for storing large-scale functional genomics data. Bioinform. 26(11): 1458-1459 (2010) - [j47]Xiaoyu Chen, Michael M. Hoffman, Jeff A. Bilmes, Jay R. Hesselberth, William Stafford Noble:
A dynamic Bayesian network for identifying protein-binding footprints from single molecule-based sequencing data. Bioinform. 26(12): 334-342 (2010) - [j46]Zafer Aydin, John I. Murray, Robert H. Waterston, William Stafford Noble:
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. BMC Bioinform. 11: 84 (2010) - [j45]Martial Hue, Michael Riffle, Jean-Philippe Vert, William Stafford Noble:
Large-scale prediction of protein-protein interactions from structures. BMC Bioinform. 11: 144 (2010) - [j44]Jens Gramm, Richard M. Karp, William S. Noble, Roded Sharan, Qianfei Wang, Nir Yosef:
Prediction of Phenotype Information from Genotype Data. Commun. Inf. Syst. 10(2): 99-114 (2010) - [j43]Sheila M. Reynolds, Jeff A. Bilmes, William Stafford Noble:
Learning a Weighted Sequence Model of the Nucleosome Core and Linker Yields More Accurate Predictions in Saccharomyces cerevisiae and Homo sapiens. PLoS Comput. Biol. 6(7) (2010) - [j42]Phaedra Agius, Aaron Arvey, William Chang, William Stafford Noble, Christina S. Leslie:
High Resolution Models of Transcription Factor-DNA Affinities Improve In Vitro and In Vivo Binding Predictions. PLoS Comput. Biol. 6(9) (2010) - [c33]Sheila M. Reynolds, Zhiping Weng, Jeff A. Bilmes, William Stafford Noble:
Predicting Nucleosome Positioning Using Multiple Evidence Tracks. RECOMB 2010: 441-455 - [r1]William Stafford Noble, Christina S. Leslie:
Learning Models of Biological Sequences. Encyclopedia of Machine Learning 2010: 590-594
2000 – 2009
- 2009
- [j41]Iain Melvin, Jason Weston, Christina S. Leslie, William Stafford Noble:
RANKPROP: a web server for protein remote homology detection. Bioinform. 25(1): 121-122 (2009) - [j40]Lukas Käll, John D. Storey, William Stafford Noble:
QVALITY: non-parametric estimation of q-values and posterior error probabilities. Bioinform. 25(7): 964-966 (2009) - [j39]John Hawkins, Charles E. Grant, William Stafford Noble, Timothy L. Bailey:
Assessing phylogenetic motif models for predicting transcription factor binding sites. Bioinform. 25(12) (2009) - [j38]Timothy L. Bailey, Mikael Bodén, Fabian A. Buske, Martin C. Frith, Charles E. Grant, Luca Clementi, Jingyuan Ren, Wilfred W. Li, William Stafford Noble:
MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37(Web-Server-Issue): 202-208 (2009) - [j37]William Stafford Noble:
A Quick Guide to Organizing Computational Biology Projects. PLoS Comput. Biol. 5(7) (2009) - [c32]Sheila M. Reynolds, Jeff A. Bilmes, William Stafford Noble:
On the Relationship between DNA Periodicity and Local Chromatin Structure. RECOMB 2009: 434-450 - 2008
- [j36]Heng Lian, William A. Thompson, Robert E. Thurman, John A. Stamatoyannopoulos, William Stafford Noble, Charles E. Lawrence:
Automated mapping of large-scale chromatin structure in ENCODE. Bioinform. 24(17): 1911-1916 (2008) - [j35]Iain Melvin, Jason Weston, Christina S. Leslie, William Stafford Noble:
Combining classifiers for improved classification of proteins from sequence or structure. BMC Bioinform. 9 (2008) - [j34]Jian Qiu, William Stafford Noble:
Predicting Co-Complexed Protein Pairs from Heterogeneous Data. PLoS Comput. Biol. 4(4) (2008) - [j33]Shobhit Gupta, Jonathan H. Dennis, Robert E. Thurman, Robert E. Kingston, John A. Stamatoyannopoulos, William Stafford Noble:
Predicting Human Nucleosome Occupancy from Primary Sequence. PLoS Comput. Biol. 4(8) (2008) - [j32]Sheila M. Reynolds, Lukas Käll, Michael Riffle, Jeff A. Bilmes, William Stafford Noble:
Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks. PLoS Comput. Biol. 4(11) (2008) - [c31]Lukas Käll, John D. Storey, William Stafford Noble:
Non-parametric estimation of posterior error probabilities associated with peptides identified by tandem mass spectrometry. ECCB 2008: 42-48 - [c30]Nir Yosef, Roded Sharan, William Stafford Noble:
Improved network-based identification of protein orthologs. ECCB 2008: 200-206 - [c29]Aaron A. Klammer, Sheila M. Reynolds, Jeff A. Bilmes, Michael J. MacCoss, William Stafford Noble:
Modeling peptide fragmentation with dynamic Bayesian networks for peptide identification. ISMB 2008: 348-356 - [c28]Robert E. Thurman, William Stafford Noble, John A. Stamatoyannopoulos:
Multi-Scale Correlations in Continuous Genomic Data. Pacific Symposium on Biocomputing 2008: 201-215 - 2007
- [j31]Jian Qiu, Martial Hue, Asa Ben-Hur, Jean-Philippe Vert, William Stafford Noble:
A structural alignment kernel for protein structures. Bioinform. 23(9): 1090-1098 (2007) - [j30]Nathan Day, Andrew Hemmaplardh, Robert E. Thurman, John A. Stamatoyannopoulos, William Stafford Noble:
Unsupervised segmentation of continuous genomic data. Bioinform. 23(11): 1424-1426 (2007) - [j29]Gal Chechik, Christina S. Leslie, William Stafford Noble, Gunnar Rätsch, Quaid Morris, Koji Tsuda:
NIPS workshop on New Problems and Methods in Computational Biology. BMC Bioinform. 8(S-10) (2007) - [j28]Jean-Philippe Vert, Jian Qiu, William Stafford Noble:
A new pairwise kernel for biological network inference with support vector machines. BMC Bioinform. 8(S-10) (2007) - [j27]Iain Melvin, Eugene Ie, Rui Kuang, Jason Weston, William Stafford Noble, Christina S. Leslie:
SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition. BMC Bioinform. 8(S-4) (2007) - [j26]Iain Melvin, Eugene Ie, Jason Weston, William Stafford Noble, Christina S. Leslie:
Multi-class Protein Classification Using Adaptive Codes. J. Mach. Learn. Res. 8: 1557-1581 (2007) - [c27]Aaron A. Klammer, Xianhua Yi, Michael J. MacCoss, William Stafford Noble:
Peptide Retention Time Prediction Yields Improved Tandem Mass Spectrum Identification for Diverse Chromatography Conditions. RECOMB 2007: 459-472 - 2006
- [j25]Darrin P. Lewis, Tony Jebara, William Stafford Noble:
Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure. Bioinform. 22(22): 2753-2760 (2006) - [j24]Asa Ben-Hur, William Stafford Noble:
Choosing negative examples for the prediction of protein-protein interactions. BMC Bioinform. 7(S-1) (2006) - [j23]Jason Weston, Rui Kuang, Christina S. Leslie, William Stafford Noble:
Protein Ranking by Semi-Supervised Network Propagation. BMC Bioinform. 7(S-1) (2006) - [j22]Tobias P. Mann, Richard Humbert, John A. Stamatoyannopoulos, William Stafford Noble:
Automated Validation of Polymerase Chain Reaction Amplicon Melting Curves. J. Bioinform. Comput. Biol. 4(2): 299-316 (2006) - [c26]Darrin P. Lewis, Tony Jebara, William Stafford Noble:
Nonstationary kernel combination. ICML 2006: 553-560 - [c25]Tobias P. Mann, William Stafford Noble:
Efficient identification of DNA hybridization partners in a sequence database. ISMB (Supplement of Bioinformatics) 2006: 350-358 - [p2]Jason Weston, Christina S. Leslie, Eugene Ie, William Stafford Noble:
Semi-Supervised Protein Classification Using Cluster Kernels. Semi-Supervised Learning 2006: 342-360 - [i1]Jean-Philippe Vert, Jian Qiu, William Stafford Noble:
Metric learning pairwise kernel for graph inference. CoRR abs/q-bio/0610040 (2006) - 2005
- [j21]Jason Weston, Christina S. Leslie, Eugene Ie, Dengyong Zhou, André Elisseeff, William Stafford Noble:
Semi-supervised protein classification using cluster kernels. Bioinform. 21(15): 3241-3247 (2005) - [j20]Rui Kuang, Jason Weston, William Stafford Noble, Christina S. Leslie:
Motif-based protein ranking by network propagation. Bioinform. 21(19): 3711-3718 (2005) - [j19]Charles X. Ling, William Stafford Noble, Qiang Yang:
Guest Editors' Introduction to the Special Issue: Machine Learning for Bioinformatics - Part 1. IEEE ACM Trans. Comput. Biol. Bioinform. 2(2): 81-82 (2005) - [j18]Charles X. Ling, William Stafford Noble, Qiang Yang:
Guest Editor's Introduction to the Special Issue: Machine Learning for Bioinformatics-Part 2. IEEE ACM Trans. Comput. Biol. Bioinform. 2(3): 177-178 (2005) - [c24]Will Sheffler, Eli Upfal, John Sedivy, William Stafford Noble:
A Learned Comparative Expression Measure for Affymetrix GeneChip DNA Microarrays. CSB 2005: 144-154 - [c23]Aaron A. Klammer, Christine C. Wu, Michael J. MacCoss, William Stafford Noble:
Peptide Charge State Determination for Low-Resolution Tandem Mass Spectra. CSB 2005: 175-185 - [c22]Tobias P. Mann, Richard Humbert, John A. Stamatoyannopoulos, William Stafford Noble:
Automated Validation of Polymerase Chain Reactions Using Amplicon Melting Curves. CSB 2005: 377-385 - [c21]Eugene Ie, Jason Weston, William Stafford Noble, Christina S. Leslie:
Multi-class protein fold recognition using adaptive codes. ICML 2005: 329-336 - [c20]Asa Ben-Hur, William Stafford Noble:
Kernel methods for predicting protein-protein interactions. ISMB (Supplement of Bioinformatics) 2005: 38-46 - [c19]William Stafford Noble, Scott Kuehn, Robert E. Thurman, Man Yu, John A. Stamatoyannopoulos:
Predicting the in vivo signature of human gene regulatory sequence. ISMB (Supplement of Bioinformatics) 2005: 328-343 - [c18]Jean-Philippe Vert, Robert E. Thurman, William Stafford Noble:
Kernels for gene regulatory regions. NIPS 2005: 1401-1408 - 2004
- [j17]Christina S. Leslie, Eleazar Eskin, Adiel Cohen, Jason Weston, William Stafford Noble:
Mismatch string kernels for discriminative protein classification. Bioinform. 20(4): 467-476 (2004) - [j16]Paul Pavlidis, Ilan Wapinski, William Stafford Noble:
Support vector machine classification on the web. Bioinform. 20(4): 586-587 (2004) - [j15]Wei Wu, William Stafford Noble:
Genomic data visualization on the Web. Bioinform. 20(11): 1804-1805 (2004) - [j14]Gert R. G. Lanckriet, Tijl De Bie, Nello Cristianini, Michael I. Jordan, William Stafford Noble:
A statistical framework for genomic data fusion. Bioinform. 20(16): 2626-2635 (2004) - [c17]Koji Tsuda, William Stafford Noble:
Learning kernels from biological networks by maximizing entropy. ISMB/ECCB (Supplement of Bioinformatics) 2004: 326-333 - [c16]Gert R. G. Lanckriet, Minghua Deng, Nello Cristianini, Michael I. Jordan, William Stafford Noble:
Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast. Pacific Symposium on Biocomputing 2004: 300-311 - 2003
- [j13]Paul Pavlidis, William Stafford Noble:
Matrix2png: a utility for visualizing matrix data. Bioinform. 19(2): 295-296 (2003) - [j12]Paul Pavlidis, Qinghong Li, William Stafford Noble:
The effect of replication on gene expression microarray experiments. Bioinform. 19(13): 1620-1627 (2003) - [j11]Shawn M. Gomez, William Stafford Noble, Andrey Rzhetsky:
Learning to predict protein-protein interactions from protein sequences. Bioinform. 19(15): 1875-1881 (2003) - [j10]Jie Qin, Darrin P. Lewis, William Stafford Noble:
Kernel hierarchical gene clustering from microarray expression data. Bioinform. 19(16): 2097-2104 (2003) - [j9]Eleazar Eskin, William Stafford Noble, Yoram Singer:
Protein Family Classification Using Sparse Markov Transducers. J. Comput. Biol. 10(2): 187-213 (2003) - [j8]Li Liao, William Stafford Noble:
Combining Pairwise Sequence Similarity and Support Vector Machines for Detecting Remote Protein Evolutionary and Structural Relationships. J. Comput. Biol. 10(6): 857-868 (2003) - [c15]Timothy L. Bailey, William Stafford Noble:
Searching for statistically significant regulatory modules. ECCB 2003: 16-25 - [c14]Jason Weston, Christina S. Leslie, Dengyong Zhou, André Elisseeff, William Stafford Noble:
Semi-supervised Protein Classification Using Cluster Kernels. NIPS 2003: 595-602 - 2002
- [j7]Paul Pavlidis, Jason Weston, Jinsong Cai, William Stafford Noble:
Learning Gene Functional Classifications from Multiple Data Types. J. Comput. Biol. 9(2): 401-411 (2002) - [j6]Eleazar Eskin, William Stafford Noble, Yoram Singer:
Using Substitution Matrices to Estimate Probability Distributions for Biological Sequences. J. Comput. Biol. 9(6): 775-791 (2002) - [c13]Bernhard Schölkopf, Jason Weston, Eleazar Eskin, Christina S. Leslie, William Stafford Noble:
A Kernel Approach for Learning from almost Orthogonal Patterns. ECML 2002: 511-528 - [c12]Christina S. Leslie, Eleazar Eskin, Jason Weston, William Stafford Noble:
Mismatch String Kernels for SVM Protein Classification. NIPS 2002: 1417-1424 - [c11]Bernhard Schölkopf, Jason Weston, Eleazar Eskin, Christina S. Leslie, William Stafford Noble:
A Kernel Approach for Learning from Almost Orthogonal Patterns. PKDD 2002: 494-511 - [c10]Paul Pavlidis, Darrin P. Lewis, William Stafford Noble:
Exploring Gene Expression Data with Class Scores. Pacific Symposium on Biocomputing 2002: 474-485 - [c9]Christina S. Leslie, Eleazar Eskin, William Stafford Noble:
The Spectrum Kernel: A String Kernel for SVM Protein Classification. Pacific Symposium on Biocomputing 2002: 566-575 - [c8]Li Liao, William Stafford Noble:
Combining pairwise sequence similarity and support vector machines for remote protein homology detection. RECOMB 2002: 225-232 - 2001
- [c7]Eleazar Eskin, William Noble Grundy, Yoram Singer:
Using mixtures of common ancestors for estimating the probabilities of discrete events in biological sequences. ISMB (Supplement of Bioinformatics) 2001: 65-73 - [c6]Paul Pavlidis, Christopher Tang, William Stafford Noble:
Classification of genes using probabilistic models of microarray expression profiles. BIOKDD 2001: 15-21 - [c5]Paul Pavlidis, Terrence S. Furey, M. Liberto, David Haussler, William Noble Grundy:
Promoter Region-Based Classification of Genes. Pacific Symposium on Biocomputing 2001: 151-164 - [c4]Paul Pavlidis, Jason Weston, Jinsong Cai, William Noble Grundy:
Gene functional classification from heterogeneous data. RECOMB 2001: 249-255 - 2000
- [c3]Eleazar Eskin, William Noble Grundy, Yoram Singer:
Protein Family Classification Using Sparse Markov Transducers. ISMB 2000: 134-145
1990 – 1999
- 1999
- [j5]William Noble Grundy, Timothy L. Bailey:
Family pairwise search with embedded motif models. Bioinform. 15(6): 463-470 (1999) - [c2]Timothy L. Bailey, William Noble Grundy:
Classifying proteins by family using the product of correlated p-values. RECOMB 1999: 10-14 - [p1]Timothy L. Bailey, Michael E. Baker, Charles Elkan, William Noble Grundy:
MEME, MAST, and Meta-MEME: New Tools for Motif Discovery in Protein Sequences. Pattern Discovery in Biomolecular Data 1999: 30-54 - 1998
- [j4]William Noble Grundy:
Homology Detection via Family Pairwise Search. J. Comput. Biol. 5(3): 479-491 (1998) - [c1]William Noble Grundy:
Family-based homology detection via pairwise sequence comparison. RECOMB 1998: 94-100 - 1997
- [j3]William Noble Grundy, Timothy L. Bailey, Charles Elkan, Michael E. Baker:
Meta-MEME: motif-based hidden Markov models of protein families. Comput. Appl. Biosci. 13(4): 397-406 (1997) - 1996
- [j2]William Noble Grundy, Timothy L. Bailey, Charles Elkan:
ParaMEME: a parallel implementation and a web interface for a DNA and protein motif discovery tool. Comput. Appl. Biosci. 12(4): 303-310 (1996) - [j1]John Batali, William Noble Grundy:
Modeling the Evolution of Motivation. Evol. Comput. 4(3): 235-270 (1996)
Coauthor Index
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