61 citations · 69 across the 6 of their papers we have counts for
16 papers
Towards a Taxonomy of Graph Learning Datasets
Renming Liu, Semih Cantürk, Frederik Wenkel +10
Graph neural networks (GNNs) have attracted much attention due to their ability to leverage the intrinsic geometries of the underlying data. Although many different types of GNN mo…
Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning
Michael Moor, Nicolas Bennet, Drago Plecko +5
Despite decades of clinical research, sepsis remains a global public health crisis with high mortality, and morbidity. Currently, when sepsis is detected and the underlying pathoge…
Graph Kernels: State-of-the-Art and Future Challenges
Karsten Borgwardt, Elisabetta Ghisu, Felipe Llinares-López +2
Graph-structured data are an integral part of many application domains, including chemoinformatics, computational biology, neuroimaging, and social network analysis. Over the last…
Topological Data Analysis of copy number alterations in cancer
Stefan Groha, Caroline Weis, Alexander Gusev +1
Identifying subgroups and properties of cancer biopsy samples is a crucial step towards obtaining precise diagnoses and being able to perform personalized treatment of cancer patie…
Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis
Jannis Born, Nina Wiedemann, Gabriel Brändle +3
Controlling the COVID-19 pandemic largely hinges upon the existence of fast, safe, and highly-available diagnostic tools. Ultrasound, in contrast to CT or X-Ray, has many practical…
Path Imputation Strategies for Signature Models of Irregular Time Series
Michael Moor, Max Horn, Christian Bock +2
The signature transform is a 'universal nonlinearity' on the space of continuous vector-valued paths, and has received attention for use in machine learning on time series. However…