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Julia E Vogt
Tytuł
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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
2952011
Interpretability and explainability: A machine learning zoo mini-tour
R Marcinkevičs, JE Vogt
arXiv preprint arXiv:2012.01805, 2020
1392020
Introduction to Machine Learning in Digital Healthcare Epidemiology
MD Jan A. Roth, MD Manuel Battegay, MD Fabrice Juchler, ...
Infection Control & Hospital Epidemiology, 2018
782018
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
722016
Generalized Multimodal ELBO
TM Sutter, I Daunhawer, JE Vogt
The International Conference on Learning Representations (ICLR), 2021
682021
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
T Sutter, I Daunhawer, JE Vogt
Neural Information Processing Systems (NeurIPS) 2020, 2020
612020
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
542014
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
532020
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
492019
Interpretable Models for Granger Causality Using Self-explaining Neural Networks
R Marcinkevičs, JE Vogt
The International Conference on Learning Representations (ICLR), 2021
482021
Re-focusing explainability in medicine
L Arbelaez Ossa, G Starke, G Lorenzini, JE Vogt, DM Shaw, BS Elger
Digital health 8, 20552076221074488, 2022
452022
A complete analysis of the l_1, p group-lasso
J Vogt, V Roth
International Conference of Machine Learning (ICML), 2012
402012
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
362021
A Deep Variational Approach to Clustering Survival Data
L Manduchi, R Marcinkevics, MC Massi, V Gotta, T Müller, F Vasella, ...
The Eleventh International Conference on Learning Representations (ICLR) 2022, 2022
302022
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
272020
On the identifiability and estimation of causal location-scale noise models
A Immer, C Schultheiss, JE Vogt, B Schölkopf, P Bühlmann, A Marx
International Conference on Machine Learning (ICML) 2023, 14316-14332, 2023
262023
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
262019
On the limitations of multimodal VAEs
I Daunhawer, TM Sutter, K Chin-Cheong, E Palumbo, JE Vogt
The Eleventh International Conference on Learning Representations (ICLR) 2022, 2022
252022
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
252010
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
242020
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