William Stafford Noble
Affiliations: | 1999-2002 | Computer Science | Columbia University, New York, NY |
| 2002- | Genome Sciences | University of Washington, Seattle, Seattle, WA |
Area:
modeling biological processes at the molecular levelWebsite:
http://www.gs.washington.edu/faculty/noble.htmGoogle:
"William Noble"Bio:
http://noble.gs.washington.edu/
Mean distance: 19.33 (cluster 23)
Cross-listing: Computational Biology Tree
Parents
Sign in to add mentor| Charles Elkan | grad student | 1998 | UCSD (Computational Biology Tree) | |
| (A Bayesian approach to motif-based protein modeling) | ||||
| David Henry Haussler | post-doc | 1999 | UC Santa Cruz (Computational Biology Tree) | |
Children
Sign in to add trainee| Darrin P. Lewis | grad student | Columbia | |
| Lindsay Pino | grad student | University of Washington (Chemistry Tree) | |
| Aaron A. Klammer | grad student | 2008 | University of Washington |
| Xiaoyu Chen | grad student | 2011 | University of Washington |
| Benjamin J. Diament | grad student | 2011 | University of Washington |
| Oliver Serang | grad student | 2011 | University of Washington |
| Ritambhara Singh | post-doc | University of Washington (MathTree) | |
| William E Fondrie | post-doc | 2018- | University of Washington (Chemistry Tree) |
| Michael M. Hoffman | post-doc | 2008-2013 | University of Washington (Computational Biology Tree) |
BETA: Related publications
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Publications
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| Harris L, Fondrie WE, Oh S, et al. (2023) Evaluating Proteomics Imputation Methods with Improved Criteria. Journal of Proteome Research. 22: 3427-3438 |
| Dekker J, Alber F, Aufmkolk S, et al. (2023) Spatial and temporal organization of the genome: Current state and future aims of the 4D nucleome project. Molecular Cell |
| Nii Adoquaye Acquaye FL, Kertesz-Farkas A, Noble WS. (2023) Efficient Indexing of Peptides for Database Search Using Tide. Journal of Proteome Research. 22: 577-584 |
| Kertesz-Farkas A, Nii Adoquaye Acquaye FL, Bhimani K, et al. (2023) The Crux Toolkit for Analysis of Bottom-Up Tandem Mass Spectrometry Proteomics Data. Journal of Proteome Research |
| Dincer AB, Lu Y, Schweppe DK, et al. (2022) Reducing Peptide Sequence Bias in Quantitative Mass Spectrometry Data with Machine Learning. Journal of Proteome Research |
| Heil LR, Fondrie WE, McGann CD, et al. (2022) Building Spectral Libraries from Narrow-Window Data-Independent Acquisition Mass Spectrometry Data. Journal of Proteome Research. 21: 1382-1391 |
| Phipps WS, Smith KD, Yang HY, et al. (2022) Tandem Mass Spectrometry-Based Amyloid Typing Using Manual Microdissection and Open-Source Data Processing. American Journal of Clinical Pathology. 157: 748-757 |
| Demetci P, Santorella R, Sandstede B, et al. (2022) SCOT: Single-Cell Multi-Omics Alignment with Optimal Transport. Journal of Computational Biology : a Journal of Computational Molecular Cell Biology. 29: 3-18 |
| Demetci P, Santorella R, Sandstede B, et al. (2022) Single-Cell Multiomics Integration by SCOT. Journal of Computational Biology : a Journal of Computational Molecular Cell Biology |
| Whalen S, Schreiber J, Noble WS, et al. (2021) Navigating the pitfalls of applying machine learning in genomics. Nature Reviews. Genetics |