Single-cell atlases promise to provide a 'missing link'between genes, diseases and
therapies. By identifying the specific cell types, states, programs and contexts where disease …

ABSTRACT Introduction The concept of Digital Twins (DTs) translated to drug development
and clinical trials describes virtual representations of systems of various complexities …

Generative pretrained models have achieved remarkable success in various domains such
as language and computer vision. Specifically, the combination of large-scale diverse …

Causal disentanglement aims to uncover a representation of data using latent variables that
are interrelated through a causal model. Such a representation is identifiable if the latent …

Large pretrained models have become foundation models leading to breakthroughs in
natural language processing and related fields. Developing foundation models for …

Recent efforts to construct reference maps of cellular phenotypes have expanded the
volume and diversity of single-cell omics data, providing an unprecedented resource for …

Biomedical data are amassed at an ever-increasing rate, and machine learning tools that
use prior knowledge in combination with biomedical big data are gaining much traction 1, 2 …

Biolord is a deep generative method for disentangling single-cell multi-omic data to known
and unknown attributes, including spatial, temporal and disease states, used to reveal the …

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 …

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene
expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has …