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Kathy Li
Kathy Li
Verified email at columbia.edu
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Year
Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data
K Li, I Urteaga, CH Wiggins, A Druet, A Shea, VJ Vitzthum, N Elhadad
NPJ digital medicine 3 (1), 79, 2020
372020
A simple developmental model recapitulates complex insect wing venation patterns
J Hoffmann, S Donoughe, K Li, MK Salcedo, CH Rycroft
Proceedings of the National Academy of Sciences 115 (40), 9905-9910, 2018
322018
Dose–response modeling in high-throughput cancer drug screenings: an end-to-end approach
W Tansey, K Li, H Zhang, SW Linderman, R Rabadan, DM Blei, ...
Biostatistics 23 (2), 643-665, 2022
72022
A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking
K Li, I Urteaga, A Shea, VJ Vitzthum, CH Wiggins, N Elhadad
Journal of the American Medical Informatics Association 29 (1), 3-11, 2022
72022
A generative modeling approach to calibrated predictions: a use case on menstrual cycle length prediction
I Urteaga, K Li, A Shea, VJ Vitzthum, CH Wiggins, N Elhadad
Machine Learning for Healthcare Conference, 535-566, 2021
32021
Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data. npj Digital Med. 3, 1–13
K Li
32020
Ribotoxic collisions on CAG expansions disrupt proteostasis and stress responses in Huntington's Disease
R Aviner, TT Lee, VB Masto, D Gestaut, KH Li, RP Andino, J Frydman
BiorXiv, 2022.05. 04.490528, 2022
22022
Dose-response modeling in high-throughput cancer drug screenings: a case study with recommendations for practitioners
W Tansey, K Li, H Zhang, SW Linderman, R Rabadan, DM Blei, ...
arXiv preprint arXiv:1812.05691, 2018
22018
A generative, predictive model for menstrual cycle lengths that accounts for potential self-tracking artifacts in mobile health data
K Li, I Urteaga, A Shea, VJ Vitzthum, CH Wiggins, N Elhadad
arXiv preprint arXiv:2102.12439, 2021
12021
Learning predictive models from menstrual cycle data
K Li
Columbia University, 2022
2022
Author Correction: Cotranslational prolyl hydroxylation is essential for flavivirus biogenesis
R Aviner, KH Li, J Frydman, R Andino
Nature 599 (7885), E3-E3, 2021
2021
Author Correction: Genomic epidemiology reveals multiple introductions of SARS-CoV-2 from mainland Europe into Scotland
JG Shepherd, T Williams, J Hughes, AC Elihu, A Patawee, A Shirin, ...
Nature Microbiology 6 (3), 414-414, 2021
2021
Publisher Correction: Genomic epidemiology reveals multiple introductions of SARS-CoV-2 from mainland Europe into Scotland
A da Silva Filipe, JG Shepherd, T Williams, J Hughes, E Aranday-Cortes, ...
Nature microbiology 6 (2), 271-271, 2021
2021
Publisher Correction: Genomic epidemiology reveals multiple introductions of SARS-CoV-2 from mainland Europe into Scotland
JG Shepherd, T Williams, J Hughes, AC Elihu, A Patawee, A Shirin, ...
Nature Microbiology 6 (2), 271-271, 2021
2021
Global proteomic and transcriptomic analysis of the LRRK2 G2019S mutation in iPSC-derived midbrain dopaminergic neurons
J Martin, H Booth, B Gao, K Li, C Roberts, N Allaire, J Vowles, S Cowley, ...
MOLECULAR & CELLULAR PROTEOMICS 16 (8), S52-S52, 2017
2017
High throughput proteomics approach reveals mechanistic basis of acute viral pathogenesis mediated by an RNAi suppressor protein in insect cells
A Nayak, MJ Trnka, K Li, C Kerr, E Jan, AL Burlingame, R Andino
MOLECULAR & CELLULAR PROTEOMICS 16 (8), S49-S49, 2017
2017
Unraveling White Lupin's Signal Transduction in Response to Phosphorus Deficiency Using iTRAQ Labeling, Phosphopeptide Enrichment, and Tandem Mass Spectrometry
M Amadi, J Cole, K Li, RJ Chalkley, A Burlingame, C Uhde‐Stone
The FASEB Journal 31, 617.1-617.1, 2017
2017
A Multiple Sclerosis Disease-Risk Variant in EVI5 Links Susceptibility to the S1P Pathway
A Didonna, N Isobe, SJ Caillier, KH Li, AL Burlingame, SL Hauser, ...
ANNALS OF NEUROLOGY 78, S62-S63, 2015
2015
Application of stable isotope labeling-based quantitative PTM proteomics and interactomics in study of Arabidopsis cell signaling
EOY Wong, MH Leung, N Yang, J Ren, S Xu, S Nair, K Li, Z Al Wang, ...
Rethinking dose-response modeling in high throughput cancer drug screenings: a case study and recommendations for practitioners
W Tansey, K Li, H Zhang, SW Linderman, DM Blei, R Rabadan, ...
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Articles 1–20