Deep generative models learn highly complex and non-linear representations to generate
realistic synthetic data. While they have achieved notable success in computer vision and …

Chronic kidney disease (CKD) is a major worldwide health concern because of its
progressive nature and complex biology. Traditional diagnostic and therapeutic approaches …

Cancer clinics capture disease data at various scales, from genetic to organ level. Current
bioinformatic methods struggle to handle the heterogeneous nature of this data, especially …

Survival prediction, central to the analysis of clinical trials, has the potential to be
transformed by the availability of RNA-seq data as it reveals the underlying molecular and …

This paper addresses unsupervised representation learning on tabular data containing
multiple views generated by distinct sources of measurement. Traditional methods, which …

Graph generation is a fundamental task in machine learning, and it is critical for numerous
real-world applications, biomedical discovery and social science. Existing diffusion-based …

Despite the widespread utilization of post-hoc explanation methods for graph neural
networks (GNNs) in high-stakes settings, there has been a lack of comprehensive evaluation …

Despite the widespread use of unsupervised Graph Neural Networks (GNNs), their post-hoc
explainability remains underexplored. Current graph explanation methods typically focus on …