Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

Background The single cell RNA sequencing (scRNA-seq) technique begin a new era by
allowing the observation of gene expression at the single cell level. However, there is also a …

It is currently challenging to analyze single-cell data consisting of many cells and samples,
and to address variations arising from batch effects and different sample preparations. For …

Over the past few years, we have seen fundamental breakthroughs in core problems in
machine learning, largely driven by advances in deep neural networks. At the same time, the …

As a fundamental unit of life, the cell has rightfully been the subject of intense investigation
throughout the history of biology. Technical innovations now make it possible to assay …

We present a probabilistic deep learning methodology that enables the construction of
predictive data-driven surrogates for stochastic systems. Leveraging recent advances in …

Motivation Intra-tumor heterogeneity is one of the key confounding factors in deciphering
tumor evolution. Malignant cells exhibit variations in their gene expression, copy numbers …

Classifying all cells in an organ is a relevant and difficult problem from plant developmental
biology. We here abstract the problem into a new benchmark for node classification in a geo …

Context‐aware recommendation systems attempt to address the challenge of identifying
products or items that have the greatest chance of meeting user requirements by adapting to …

Differential gene expression analysis from RNA-sequencing (RNA-seq) data offers crucial
insights into biological differences between sample groups. However, the conventional …