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
Interpretability and explainability: A machine learning zoo mini-tour
R Marcinkevičs, JE Vogt
arXiv preprint arXiv:2012.01805, 2020
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
Introduction to Machine Learning in Digital Healthcare Epidemiology
MD Jan A. Roth, MD Manuel Battegay, MD Fabrice Juchler, ...
Infection Control & Hospital Epidemiology, 2018
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
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
A complete analysis of the l_1, p group-lasso
J Vogt, V Roth
International Conference of Machine Learning (ICML), 2012
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
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
T Sutter, I Daunhawer, JE Vogt
Advances in Neural Information Processing Systems 33 pre-proceedings …, 2020
The translation-invariant Wishart-Dirichlet process for clustering distance data
JE Vogt, S Prabhakaran, TJ Fuchs, V Roth
Proceedings of the 27th International Conference on Machine Learning (ICML …, 2010
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
Generalized Multimodal ELBO
TM Sutter, I Daunhawer, JE Vogt
The International Conference on Learning Representations (ICLR), 2021
The Group-Lasso: ℓ1, ∞  Regularization versus ℓ1,2 Regularization
JE Vogt, V Roth
Pattern Recognition: 32nd DAGM Symposium, Darmstadt, Germany, September 22 …, 2010
Automatic model selection in archetype analysis
S Prabhakaran, S Raman, JE Vogt, V Roth
Pattern Recognition: Joint 34th DAGM and 36th OAGM Symposium, Graz, Austria …, 2012
Re-focusing explainability in medicine
L Arbelaez Ossa, G Starke, G Lorenzini, JE Vogt, DM Shaw, BS Elger
Digital Health 8, 20552076221074488, 2022
A Deep Variational Approach to Clustering Survival Data
L Manduchi, R Marcinkevics, MC Massi, V Gotta, T Müller, F Vasella, ...
ICLR 2022, 2022
DPSOM: Deep probabilistic clustering with self-organizing maps
L Manduchi, M Hüser, J Vogt, G Rätsch, V Fortuin
arXiv preprint arXiv:1910.01590, 2019
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
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
Self-supervised Disentanglement of Modality-specific and Shared Factors Improves Multimodal Generative Models
I Daunhawer, TM Sutter, R Marcinkevics, JE Vogt
German Conference on Pattern Recognition DAGM-GCPR, 2020
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