Kristen M. Naegle
Cited by
Cited by
Injury-induced HDAC5 nuclear export is essential for axon regeneration
Y Cho, R Sloutsky, KM Naegle, V Cavalli
Cell 155 (4), 894-908, 2013
Avoiding common pitfalls when clustering biological data
T Ronan, Z Qi, KM Naegle
Science signaling 9 (432), re6-re6, 2016
Phosphoproteomics of collagen receptor networks reveals SHP-2 phosphorylation downstream of wild-type DDR2 and its lung cancer mutants
LK Iwai, LS Payne, MT Luczynski, F Chang, H Xu, RW Clinton, A Paul, ...
Biochemical Journal 454 (3), 501-513, 2013
Different Epidermal Growth Factor Receptor (EGFR) Agonists Produce Unique Signatures for the Recruitment of Downstream Signaling Proteins*♦
T Ronan, JL Macdonald-Obermann, L Huelsmann, NJ Bessman, ...
Journal of Biological Chemistry 291 (11), 5528-5540, 2016
Predicting patient response to the antiarrhythmic mexiletine based on genetic variation: personalized medicine for long QT syndrome
W Zhu, A Mazzanti, TL Voelker, P Hou, JD Moreno, P Angsutararux, ...
Circulation research 124 (4), 539-552, 2019
ProteomeScout: a repository and analysis resource for post-translational modifications and proteins
MK Matlock, AS Holehouse, KM Naegle
Nucleic acids research 43 (D1), D521-D530, 2015
PTMScout, a Web resource for analysis of high throughput post-translational proteomics studies
KM Naegle, M Gymrek, BA Joughin, JP Wagner, RE Welsch, MB Yaffe, ...
Molecular & Cellular Proteomics 9 (11), 2558-2570, 2010
MCAM: multiple clustering analysis methodology for deriving hypotheses and insights from high-throughput proteomic datasets
KM Naegle, RE Welsch, MB Yaffe, FM White, DA Lauffenburger
PLoS Computational Biology 7 (7), e1002119, 2011
A crowdsourcing approach to developing and assessing prediction algorithms for AML prognosis
DP Noren, BL Long, R Norel, K Rrhissorrakrai, K Hess, CW Hu, ...
PLoS computational biology 12 (6), e1004890, 2016
Criteria for biological reproducibility: What does “n” mean?
K Naegle, NR Gough, MB Yaffe
Science signaling 8 (371), fs7-fs7, 2015
Accounting for noise when clustering biological data
R Sloutsky, N Jimenez, SJ Swamidass, KM Naegle
Briefings in bioinformatics 14 (4), 423-436, 2013
An integrated comparative phosphoproteomic and bioinformatic approach reveals a novel class of MPM-2 motifs upregulated in EGFRvIII-expressing glioblastoma cells
BA Joughin, KM Naegle, PH Huang, MB Yaffe, DA Lauffenburger, ...
Molecular BioSystems 5 (1), 59-67, 2009
Robust co-regulation of tyrosine phosphorylation sites on proteins reveals novel protein interactions
KM Naegle, FM White, DA Lauffenburger, MB Yaffe
Molecular BioSystems 8 (10), 2771-2782, 2012
Ten simple rules for effective presentation slides
KM Naegle
PLoS computational biology 17 (12), e1009554, 2021
Openensembles: a python resource for ensemble clustering
T Ronan, S Anastasio, Z Qi, PHSV Tavares, R Sloutsky, KM Naegle
Journal of Machine Learning Research 19 (26), 1-6, 2018
ProteoClade: A taxonomic toolkit for multi-species and metaproteomic analysis
AD Mooradian, S Van Der Post, KM Naegle, JM Held
PLoS computational biology 16 (3), e1007741, 2020
A path to translation: How 3D patient tumor avatars enable next generation precision oncology
S Bose, M Barroso, MG Chheda, H Clevers, E Elez, S Kaochar, SE Kopetz, ...
Cancer Cell 40 (12), 1448-1453, 2022
KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data
S Crowl, BT Jordan, H Ahmed, CX Ma, KM Naegle
Nature communications 13 (1), 4283, 2022
Defining phenotypic and functional heterogeneity of glioblastoma stem cells by mass cytometry
L Galdieri, A Jash, O Malkova, DD Mao, P DeSouza, YE Chu, A Salter, ...
JCI insight 6 (4), 2021
Reproducible analysis of post-translational modifications in proteomes—Application to human mutations
AS Holehouse, KM Naegle
PloS one 10 (12), e0144692, 2015
The system can't perform the operation now. Try again later.
Articles 1–20