Qiang Kang

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

The paper presents a unified spatial transcriptomic dataset of 10 mouse organs, providing high-resolution expression matrices paired with histological images for method development and benchmarking.

BiOmics introduces an AI agent that improves multi-omics interpretation by combining reasoning and embedding spaces, outperforming existing methods in variant discovery, cell annotation, and drug repurposing.

CellBin processes spatial omics data to create cell-level matrices, outperforming existing methods across multiple platforms and omics types.

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.

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

CellBinDB provides a large-scale multimodal annotated dataset for cell segmentation and benchmarks seven state-of-the-art methods.

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.

BatchEval Pipeline evaluates batch effects in multi-dataset transcriptomics analysis and provides a report to guide batch correction.

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

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