Heterogeneity describes the differences among cancer cells within and between tumors. It
refers to cancer cells describing variations in morphology, transcriptional profiles …

Integrating single-cell multi-omics data is a challenging task that has led to new insights into
complex cellular systems. Various computational methods have been proposed to effectively …

The development of single-cell multi-omics technology has greatly enhanced our
understanding of biology, and in parallel, numerous algorithms have been proposed to …

Bulk sequencing methodologies have allowed us to make great progress in cancer
research. Unfortunately, these techniques lack the resolution to fully unravel the epigenetic …

Large language models (LLMs) are a class of artificial intelligence models based on deep
learning, which have great performance in various tasks, especially in natural language …

Neural networks such as variational autoencoders (VAE) perform dimensionality reduction
for the visualization and analysis of genomic data, but are limited in their interpretability: it is …

Single-cell multiview clustering is essential for analyzing the different cell subtypes of the
same cell from different views. Some attempts have been made, but most of these models …

Cells are the fundamental units of biological systems and exhibit unique development
trajectories and molecular features. Our exploration of how the genomes orchestrate the …

The transformers have achieved significant accomplishments in the natural language
processing as its outstanding parallel processing capabilities and highly flexible attention …

Multi-modal learning is essential for understanding information in the real world. Jointly
learning from multi-modal data enables global integration of both shared and modality …