Yong Zhang

DCAF improves single-cell perturbation simulation by addressing data, evaluation, and model validity, outperforming baselines in generalization and recapitulating regulatory heterogeneity in mouse brain.

DCSMAP improves read alignment accuracy and efficiency by selectively applying pangenome graph alignment to reads with unreliable linear placements.

The paper evaluates multi-view gene representations under a matched contract, showing condition-dependent fusion operator rankings across different view combinations and tasks.

OrionGeno annotates over 5,300 eukaryotic genomes using a phylogeny-aware deep learning model, outperforming existing methods in gene and protein structure prediction.

OmniCell integrates single-cell and spatial transcriptomics to model tissue context, revealing how gene programs operate in different anatomical environments.

The paper presents a spatiotemporal transcriptome database of human embryogenesis, providing spatial gene expression and substructure data across 50 developing organs.

RegFormer improves single-cell RNA sequencing analysis by integrating gene regulatory networks with Mamba-based modeling, outperforming existing models in clustering, integration, and annotation.

ExMODE is a multi-omics database for extremophiles that integrates and standardizes data to enable comparative analysis and bioprospecting.

DCS Tools provides a high-performance, resource-efficient genomic analysis suite with significant speedup and storage reduction for population-scale sequencing data.

DPGT is a high-performance joint variant calling tool for large cohorts, offering comparable accuracy to existing methods with improved speed and scalability.

NanoporeDB is a structural database of multimeric protein nanopores, providing 3D models and quantitative metrics to support nanopore-based sensing and synthetic biology.

The paper introduces BioPrinCRISPR, a framework to identify and characterize CRISPR-Cas systems, revealing non-canonical variants and novel domain architectures across bacterial genomes.

DynPerturb models spatiotemporal perturbation dynamics in single-cell and spatial transcriptomics, outperforming existing methods in regulatory link prediction and uncovering dynamic regulatory rewiring in biological systems.

The paper develops a method to predict soil probiotics using enhanced foundation model representations and a stacked aggregation classifier, achieving high accuracy and AUC on both balanced and imbalanced datasets.

SpaSEG is a convolutional neural network model for analyzing spatially resolved transcriptomics data, showing superior reliability and efficiency in detecting spatial patterns and intercellular interactions.

UniCell improves cell type annotation and cross-dataset harmonization by combining Cell Ontology with transcriptomic data, outperforming existing tools in accuracy and rare cell detection.

Stereopy provides a framework for analyzing multi-sample spatial transcriptomics data to model comparative and spatiotemporal cellular heterogeneity through three key algorithms.

This study shows targeting ZNF638 induces viral mimicry and enhances immune checkpoint inhibition in glioblastoma by increasing dsRNA signaling and PD-L1 expression.

ST-GEARS improves spatial information recovery in 3D spatial transcriptomics by aligning sections and correcting distortions, enhancing accuracy for downstream analysis.

This paper introduces SSGATE, a dual-path graph attention auto-encoder for integrating single-cell and spatial multi-omics data, showing improved performance and robustness over existing methods.

The paper updates a software tool to generate single-cell spatial gene expression profiles using cell boundary images from Stereo-seq data, showing improved performance on mouse liver and Arabidopsis seed datasets.

SAW improves the efficiency and accuracy of Stereo-Seq spatial transcriptomics analysis by optimizing key steps and reducing runtime.

spatiAlign integrates spatially resolved transcriptomics datasets using contrastive learning, improving batch effect removal and enabling downstream analysis across multiple tissue sections.

STOmicsDB provides a database for spatial transcriptomics data with tools for dataset visualization, publication search, and data archiving.

This paper proposes a hybrid quantum algorithm for genome assembly using short and long reads to address repetitive sequences and sequencing errors, showing improved convergence and optimization.

CellBin processes high-resolution spatial transcriptomics data to generate accurate single-cell gene expression profiles, improving accuracy and efficiency in mouse brain tissue analysis.

Spatial-ID improves cell type annotation in spatial transcriptomics by combining transfer learning and spatial embedding, outperforming existing methods on public datasets.

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

The study uses nanopore sequencing to analyze structural variations in rhesus macaques, finding more SVs on the X chromosome and evidence of positive selection.