2 citations · 4 across the 11 of their papers we have counts for
11 papers
Universal Boosts, Specific Suppressors: Sparse Autoencoder Steering of Medical Vision-Language Models
Farhad Nooralahzadeh, Benjamin Gundersen, Nicolas Deperrois +7
Medical vision-language models (VLMs) often hallucinate findings when generating chest X-ray reports: they fabricate findings that are not present in the image, miss important ones…
Repurposing Protein Language Models for Latent Flow-Based Fitness Optimization
Amaru Caceres Arroyo, Lea Bogensperger, Ahmed Allam +3
Protein fitness optimization is challenged by a vast combinatorial landscape where high-fitness variants are extremely sparse. Many current methods either underperform or require c…
A Variational Perspective on Generative Protein Fitness Optimization
Lea Bogensperger, Dominik Narnhofer, Ahmed Allam +2
The goal of protein fitness optimization is to discover new protein variants with enhanced fitness for a given use. The vast search space and the sparsely populated fitness landsca…
TAMER: A Test-Time Adaptive MoE-Driven Framework for EHR Representation Learning
Yinghao Zhu, Xiaochen Zheng, Ahmed Allam +1
We propose TAMER, a Test-time Adaptive MoE-driven framework for Electronic Health Record (EHR) Representation learning. TAMER introduces a framework where a Mixture-of-Experts (MoE…
Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis
Cécile Trottet, Manuel Schürch, Ahmed Allam +11
We propose a deep generative approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories, with a particular focus on Systemic Scle…
Clustering of Disease Trajectories with Explainable Machine Learning: A Case Study on Postoperative Delirium Phenotypes
Xiaochen Zheng, Manuel Schürch, Xingyu Chen +5
The identification of phenotypes within complex diseases or syndromes is a fundamental component of precision medicine, which aims to adapt healthcare to individual patient charact…
Attention-based Multi-task Learning for Base Editor Outcome Prediction
Amina Mollaysa, Ahmed Allam, Michael Krauthammer
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 targ…
Attention-based Multi-task Learning for Base Editor Outcome Prediction
Amina Mollaysa, Ahmed Allam, Michael Krauthammer
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 targ…
Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes
Cécile Trottet, Manuel Schürch, Ahmed Allam +6
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 fin…
Two-Stage Aggregation with Dynamic Local Attention for Irregular Time Series
Xingyu Chen, Xiaochen Zheng, Amina Mollaysa +3
Irregular multivariate time series data is characterized by varying time intervals between consecutive observations of measured variables/signals (i.e., features) and varying sampl…
Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models
Manuel Schürch, Xiang Li, Ahmed Allam +4
We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies. It leverages historical patient…