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Julia E Vogt
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Interferon-induced gene expression is a stronger predictor of treatment response than IL28B genotype in patients with hepatitis C
MT Dill, FHT Duong, JE Vogt, S Bibert, PY Bochud, L Terracciano, ...
Gastroenterology 140 (3), 1021-1031. e10, 2011
2922011
Gene expression analysis of biopsy samples reveals critical limitations of transcriptome‐based molecular classifications of hepatocellular carcinoma
Z Makowska, T Boldanova, D Adametz, L Quagliata, JE Vogt, MT Dill, ...
The Journal of Pathology: Clinical Research 2 (2), 80-92, 2016
602016
Pegylated IFN-α regulates hepatic gene expression through transient Jak/STAT activation
MT Dill, Z Makowska, G Trincucci, AJ Gruber, JE Vogt, M Filipowicz, ...
The Journal of clinical investigation 124 (4), 1568-1581, 2014
492014
Interpretability and explainability: A machine learning zoo mini-tour
R Marcinkevičs, JE Vogt
arXiv preprint arXiv:2012.01805, 2020
462020
Introduction to Machine Learning in Digital Healthcare Epidemiology
MD Jan A. Roth, MD Manuel Battegay, MD Fabrice Juchler, ...
Infection Control & Hospital Epidemiology, 2018
432018
Enhanced early prediction of clinically relevant neonatal hyperbilirubinemia with machine learning
I Daunhawer, S Kasser, G Koch, L Sieber, H Cakal, J Tütsch, M Pfister, ...
Pediatric research 86 (1), 122-127, 2019
352019
A complete analysis of the l_1, p group-lasso
J Vogt, V Roth
International Conference of Machine Learning (ICML), 2012
352012
Pharmacometrics and machine learning partner to advance clinical data analysis
G Koch, M Pfister, I Daunhawer, M Wilbaux, S Wellmann, JE Vogt
Clinical Pharmacology & Therapeutics 107 (4), 926-933, 2020
252020
The translation-invariant Wishart-Dirichlet process for clustering distance data
JE Vogt, S Prabhakaran, TJ Fuchs, V Roth
ICML, 2010
232010
Beyond the randomized clinical trial: innovative data science to close the pediatric evidence gap
SC Goulooze, LB Zwep, JE Vogt, EHJ Krekels, T Hankemeier, ...
Clinical Pharmacology & Therapeutics 107 (4), 786-795, 2020
202020
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
T Sutter, I Daunhawer, JE Vogt
Advances in Neural Information Processing Systems 33 pre-proceedings …, 2020
192020
The Group-Lasso: ℓ1, ∞  Regularization versus ℓ1,2 Regularization
JE Vogt, V Roth
Joint Pattern Recognition Symposium, 252-261, 2010
172010
Automatic model selection in archetype analysis
S Prabhakaran, S Raman, JE Vogt, V Roth
Joint DAGM (German Association for Pattern Recognition) and OAGM Symposium …, 2012
162012
Generalized Multimodal ELBO
TM Sutter, I Daunhawer, JE Vogt
The International Conference on Learning Representations (ICLR), 2021
142021
Interpretable Models for Granger Causality Using Self-explaining Neural Networks
R Marcinkevičs, JE Vogt
The International Conference on Learning Representations (ICLR), 2021
142021
Using Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric Appendicitis
R Marcinkevics, P Reis Wolfertstetter, S Wellmann, C Knorr, JE Vogt
Frontiers in Pediatrics 9, 360, 2021
92021
Identifying key predictors of mortality in young patients on chronic haemodialysis—a machine learning approach
V Gotta, G Tancev, O Marsenic, JE Vogt, M Pfister
Nephrology Dialysis Transplantation, 2020
92020
DPSOM: deep probabilistic clustering with self-organizing maps
L Manduchi, M Hüser, J Vogt, G Rätsch, V Fortuin
arXiv preprint arXiv:1910.01590, 2019
92019
Generation of Heterogeneous Synthetic Electronic Health Records using GANs
K Chin-Cheong, T Sutter, JE Vogt
Machine Learning for Health Workshop, NeurIPS 2019, Vancouver, Canada, 2019
92019
Malaria haplotype frequency estimation
L Wigger, JE Vogt, V Roth
Statistics in medicine 32 (21), 3737-3751, 2013
92013
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Articles 1–20