The remarkable flexibility and adaptability of ensemble methods and deep learning models
have led to the proliferation of their application in bioinformatics research. Traditionally …

Single-cell transcriptomic approaches are revolutionizing neuroscience. Integrating this
wealth of data with morphology and physiology, for the comprehensive study of neuronal …

Simultaneous profiling of multiomic modalities within a single cell is a grand challenge for
single-cell biology. While there have been impressive technical innovations demonstrating …

Current biotechnologies can simultaneously measure multiple high-dimensional modalities
(eg, RNA, DNA accessibility, and protein) from the same cells. A combination of different …

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 …

The advancement of single-cell RNA-sequencing technologies has led to an explosion of
cell type definitions across multiple organs and organisms. While standards for data and …

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 …

Single-cell multimodal datasets have measured various characteristics of individual cells,
enabling a deep understanding of cellular and molecular mechanisms. However …

Paired mapping of single-cell gene expression and electrophysiology is essential to
understand gene-to-function relationships in electrogenic tissues. Here, we developed in …

Consistent identification of neurons in different experimental modalities is a key problem in
neuroscience. Although methods to perform multimodal measurements in the same set of …