Cancer has relational information residing at varying scales, modalities, and resolutions of
the acquired data, such as radiology, pathology, genomics, proteomics, and clinical records …

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 …

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

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 …

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 …

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 …