Zobrazeno 1 - 10
of 3 885
pro vyhledávání: '"A. Krauthammer"'
Autor:
Vollenweider, Michael, Schürch, Manuel, Rohrer, Chiara, Gut, Gabriele, Krauthammer, Michael, Wicki, Andreas
Precision medicine offers the potential to tailor treatment decisions to individual patients, yet it faces significant challenges due to the complex biases in clinical observational data and the high-dimensional nature of biological data. This study
Externí odkaz:
http://arxiv.org/abs/2410.00509
Publikováno v:
Proceedings of the ICML 2024 Workshop on Accessible and Effi- cient Foundation Models for Biological Discovery
De novo molecule design has become a highly active research area, advanced significantly through the use of state-of-the-art generative models. Despite these advances, several fundamental questions remain unanswered as the field increasingly focuses
Externí odkaz:
http://arxiv.org/abs/2409.00046
Autor:
Trottet, Cécile, Schürch, Manuel, Allam, Ahmed, Barua, Imon, Petelytska, Liubov, Launay, David, Airò, Paolo, Bečvář, Radim, Denton, Christopher, Radic, Mislav, Distler, Oliver, Hoffmann-Vold, Anna-Maria, Krauthammer, Michael, collaborators, the EUSTAR
We propose a deep generative approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories, with a particular focus on Systemic Sclerosis (SSc). We aim to learn temporal latent representations of the un
Externí odkaz:
http://arxiv.org/abs/2407.11427
Autor:
Zheng, Xiaochen, Schürch, Manuel, Chen, Xingyu, Komninou, Maria Angeliki, Schüpbach, Reto, Allam, Ahmed, Bartussek, Jan, Krauthammer, Michael
The identification of phenotypes within complex diseases or syndromes is a fundamental component of precision medicine, which aims to adapt healthcare to individual patient characteristics. Postoperative delirium (POD) is a complex neuropsychiatric c
Externí odkaz:
http://arxiv.org/abs/2405.03327
Autor:
Schürch, Manuel, Boos, Laura, Heinzelmann-Schwarz, Viola, Gut, Gabriele, Krauthammer, Michael, Wicki, Andreas, Consortium, Tumor Profiler
AI-driven precision oncology has the transformative potential to reshape cancer treatment by leveraging the power of AI models to analyze the interaction between complex patient characteristics and their corresponding treatment outcomes. New technolo
Externí odkaz:
http://arxiv.org/abs/2402.12190
Autor:
Trottet, Cécile, Schürch, Manuel, Allam, Ahmed, Barua, Imon, Petelytska, Liubov, Distler, Oliver, Hoffmann-Vold, Anna-Maria, Krauthammer, Michael, collaborators, the EUSTAR
In this paper, we propose a deep generative time series approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories. We aim to find meaningful temporal latent representations of an underlying generati
Externí odkaz:
http://arxiv.org/abs/2311.08149
Autor:
Chen, Xingyu, Zheng, Xiaochen, Mollaysa, Amina, Schürch, Manuel, Allam, Ahmed, Krauthammer, Michael
Irregular multivariate time series data is characterized by varying time intervals between consecutive observations of measured variables/signals (i.e., features) and varying sampling rates (i.e., recordings/measurement) across these features. Modeli
Externí odkaz:
http://arxiv.org/abs/2311.07744
Human genetic diseases often arise from point mutations, emphasizing the critical need for precise genome editing techniques. Among these, base editing stands out as it allows targeted alterations at the single nucleotide level. However, its clinical
Externí odkaz:
http://arxiv.org/abs/2311.07636
Autor:
Schürch, Manuel, Li, Xiang, Allam, Ahmed, Rathmes, Giulia, Mollaysa, Amina, Cavelti-Weder, Claudia, Krauthammer, Michael
Publikováno v:
Machine Learning for Health (ML4H) 2023
We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies. It leverages historical patient trajectory data to jointly learn the generation of realistic personal
Externí odkaz:
http://arxiv.org/abs/2309.16521
Autor:
Kim, Sanghwan, Nooralahzadeh, Farhad, Rohanian, Morteza, Fujimoto, Koji, Nishio, Mizuho, Sakamoto, Ryo, Rinaldi, Fabio, Krauthammer, Michael
Recent transformer-based models have made significant strides in generating radiology reports from chest X-ray images. However, a prominent challenge remains: these models often lack prior knowledge, resulting in the generation of synthetic reports t
Externí odkaz:
http://arxiv.org/abs/2305.04561