Injury-induced HDAC5 nuclear export is essential for axon regeneration Y Cho, R Sloutsky, KM Naegle, V Cavalli Cell 155 (4), 894-908, 2013 | 342 | 2013 |
Avoiding common pitfalls when clustering biological data T Ronan, Z Qi, KM Naegle Science signaling 9 (432), re6-re6, 2016 | 167 | 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 | 84 | 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 | 60 | 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 | 58 | 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 | 48 | 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 | 47 | 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 | 37 | 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 | 35 | 2016 |
Accounting for noise when clustering biological data R Sloutsky, N Jimenez, SJ Swamidass, KM Naegle Briefings in bioinformatics 14 (4), 423-436, 2013 | 34 | 2013 |
Criteria for biological reproducibility: What does “n” mean? K Naegle, NR Gough, MB Yaffe Science signaling 8 (371), fs7-fs7, 2015 | 32 | 2015 |
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 | 32 | 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 | 24 | 2012 |
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 | 21 | 2018 |
Ten simple rules for effective presentation slides KM Naegle PLoS computational biology 17 (12), e1009554, 2021 | 20 | 2021 |
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 | 19 | 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 | 17 | 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 | 14 | 2021 |
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 | 13 | 2022 |
Reproducible analysis of post-translational modifications in proteomes—Application to human mutations AS Holehouse, KM Naegle PloS one 10 (12), e0144692, 2015 | 13 | 2015 |