This document is an interview with leading researchers in the field of AI and biology,
discussing the development and applications of Large Cellular Models (LCMs) in biological …

Numerous multi-omic investigations of cancer tissue have documented varying and poor
pairwise transcript: protein quantitative correlations, and most deconvolution tools aiming to …

Deconvolution is an efficient approach for detecting cell-type-specific (cs) transcriptomic
signals without cellular segmentation. However, this type of methods may require a …

The rise of omics research, spanning genomics, transcriptomics, proteomics, and
epigenomics, has revolutionized our understanding of biological systems. While the …

Domain adaptation, a subset of transfer learning, involves generating examples from two
related but differently distributed source and target domains. This paper introduces an …

Single-cell technologies enable high-resolution, multi-dimensional analysis of molecular
profiles in cancer biology but face challenges related to low coverage and cell annotation …

Noninvasive preimplantation genetic testing for aneuploidy based on embryonic cell-free
DNA (cfDNA) released in spent embryo culture media (SECM) has brought hope in selecting …

Accurately determining cell type composition in disease-relevant tissues is crucial for
identifying disease targets. Most existing spatial transcriptomics (ST) technologies cannot …

In recent years, the number of unmanned aerial vehicle (UAV) applications has increased.
However, navigating them indoors is still tricky because no GPS signals are available, and …

Recent advances in spatial transcriptomics technology have produced full-transcriptomic
scale dataset with subcellular spatial resolutions. Here we present a new computational …