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Kristen M. Naegle
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Injury-induced HDAC5 nuclear export is essential for axon regeneration
Y Cho, R Sloutsky, KM Naegle, V Cavalli
Cell 155 (4), 894-908, 2013
3372013
Avoiding common pitfalls when clustering biological data
T Ronan, Z Qi, KM Naegle
Science signaling 9 (432), re6-re6, 2016
1652016
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
842013
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
592016
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
562019
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
472015
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
472010
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
372011
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
352016
Accounting for noise when clustering biological data
R Sloutsky, N Jimenez, SJ Swamidass, KM Naegle
Briefings in bioinformatics 14 (4), 423-436, 2013
342013
Criteria for biological reproducibility: What does “n” mean?
K Naegle, NR Gough, MB Yaffe
Science signaling 8 (371), fs7-fs7, 2015
322015
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
312009
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
242012
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
212018
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
192020
Ten simple rules for effective presentation slides
KM Naegle
PLoS computational biology 17 (12), e1009554, 2021
172021
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
152022
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
132021
Reproducible analysis of post-translational modifications in proteomes—Application to human mutations
AS Holehouse, KM Naegle
PloS one 10 (12), e0144692, 2015
132015
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
122022
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