High-throughput single-cell profiling provides an unprecedented ability to uncover the
molecular states of millions of cells. These technologies are, however, destructive to cells …

Patient-derived organoids (PDOs) can model personalized therapy responses; however,
current screening technologies cannot reveal drug response mechanisms or how tumor …

Optimal transport (OT) is a popular and powerful tool for comparing probability measures.
However, OT suffers a few drawbacks:(i) input measures required to have the same mass,(ii) …

Efficient computation of optimal transport distance between distributions is of growing
importance in data science. Sinkhorn-based methods are currently the state-of-the-art for …

Patient-derived organoids (PDOs) can model personalized therapy responses, however
current screening technologies cannot reveal drug response mechanisms or study how …

In single-cell sequencing analysis, several computational methods have been developed to
map the cellular state space, but little has been done to map or create embeddings of the …

In single-cell sequencing analysis, several computational methods have been developed to
map the cellular state space, but little has been done to map the gene space. Here, we …

The performance of unsupervised methods such as clustering depends on the choice of
distance metric between features, or ground metric. Commonly, ground metrics are decided …

Defining meaningful distances between samples in a dataset is a fundamental problem in
machine learning. Optimal Transport (OT) lifts a distance between features (the" ground …

The increased focus on evidence-based practice in the health sciences led to a plethora of
(un) organised and digitised data. In conjunction with the availability of technological …