3 citations · 7 across the 11 of their papers we have counts for
5 papers · 2 filters
Learning Genomic Sequence Representations using Graph Neural Networks over De Bruijn Graphs
Kacper Kapuśniak, Manuel Burger, Gunnar Rätsch +1
The rapid expansion of genomic sequence data calls for new methods to achieve robust sequence representations. Existing techniques often neglect intricate structural details, empha…
On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series
Rita Kuznetsova, Alizée Pace, Manuel Burger +2
Recent advances in deep learning architectures for sequence modeling have not fully transferred to tasks handling time-series from electronic health records. In particular, in prob…
Knowledge Graph Representations to enhance Intensive Care Time-Series Predictions
Samyak Jain, Manuel Burger, Gunnar Rätsch +1
Intensive Care Units (ICU) require comprehensive patient data integration for enhanced clinical outcome predictions, crucial for assessing patient conditions. Recent deep learning…
Language Model Training Paradigms for Clinical Feature Embeddings
Yurong Hu, Manuel Burger, Gunnar Rätsch +1
In research areas with scarce data, representation learning plays a significant role. This work aims to enhance representation learning for clinical time series by deriving univers…
Multi-modal Graph Learning over UMLS Knowledge Graphs
Manuel Burger, Gunnar Rätsch, Rita Kuznetsova
Clinicians are increasingly looking towards machine learning to gain insights about patient evolutions. We propose a novel approach named Multi-Modal UMLS Graph Learning (MMUGL) fo…