2 citations · 4 across the 11 of their papers we have counts for
5 papers · 1 filter
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…