Yuhui Hu

A single-cell 3D spatiotemporal multi-omics atlas from Drosophila embryogenesis to metamorphosis

The paper constructs a 3D spatiotemporal multi-omics atlas of Drosophila development, revealing gene regulatory networks and cell type dynamics across embryogenesis to metamorphosis.

CAbiNet: Joint visualization of cells and genes based on a gene-cell graph

CAbiNet jointly visualizes cells and genes using a gene-cell graph and biMAP embedding for interactive exploration and biclustering.

Spateo: multidimensional spatiotemporal modeling of single-cell spatial transcriptomics

Spateo provides a framework for spatiotemporal modeling of single-cell spatial transcriptomics data to study cell-cell interactions and organ development in 3D space.

High-resolution spatiotemporal transcriptomic maps of developing Drosophila embryos and larvae

This study uses Stereo-seq to generate high-resolution spatiotemporal transcriptomic maps of Drosophila embryos and larvae, revealing new subregions and gene regulatory networks.

The Strand-biased Transcription of SARS-CoV-2 and Unbalanced Inhibition by Remdesivir

The paper investigates SARS-CoV-2 strand-biased transcription and shows Remdesivir preferentially inhibits negative strand synthesis.

CASB: A concanavalin A-based sample barcoding strategy for single-cell sequencing

CASB is a sample barcoding method for single-cell sequencing that enables accurate multiplexing across different sequencing techniques and detects doublets with high sensitivity.

DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning

DeeReCT-APA quantitatively predicts alternative polyadenylation site usage across genes using a deep learning model, outperforming existing methods in multiple tasks.

CIGAR-seq, a CRISPR/Cas-based method for unbiased screening of novel mRNA modification regulators

CIGAR-seq identifies mRNA modification regulators through CRISPR screening and RNA modification readout, revealing NSUN6 as a novel m5C methyltransferase.

SupportNet: a novel incremental learning framework through deep learning and support data

SupportNet addresses catastrophic forgetting in incremental learning by combining deep learning with SVM to retain old knowledge while learning new data, outperforming existing methods in multiple tasks.