Tao Wang

SpatialMOC reconstructs missing spatial multi-omics data by integrating spatial context and cross-modality learning, outperforming existing methods across multiple tissues and modalities.

VISGP identifies spatially variable genes and cellular interactions from spatial transcriptomics data, outperforming existing methods in detecting ligand-receptor pairs.

This paper introduces stMSA, a deep learning model for integrating spatial omics data, improving domain detection, matching, and alignment across tissue slices.

This paper introduces MPOGAN, a generative adversarial network for designing antimicrobial peptides with multiple desired properties, showing improved performance in antimicrobial activity and reduced cytotoxicity.

This study integrates multi-omics data from Drosophila and human Alzheimer's disease models to identify pathways involved in neurodegeneration.

DisConST improves spatial domain detection in spatial transcriptomics by using distribution-aware contrastive learning, achieving better accuracy than existing methods.

The paper identifies cell type-specific and shared chromatin-associated protein co-association modules using ChIP-Seq and machine learning in K562 and HepG2 cells.

This paper introduces ALG-DDI, a multi-scale feature fusion model for drug-drug interaction prediction that outperforms existing methods by integrating attribute, local biological, and global semantic information.

This study identifies circRNAs specific to dopamine and pyramidal neurons and links them to neuropsychiatric diseases.

This paper introduces DeepPRS, a deep learning-based polygenic risk score model that outperforms existing methods in predicting risk for common diseases using genome-wide genotype data.

This paper introduces ScHiC-Rep, a framework for single-cell Hi-C clustering that improves imputation and feature extraction, outperforming existing methods on human and mouse datasets.

xQTLImp imputes missing xQTL summary statistics using multivariate Gaussian approximation, rediscovering novel significant eQTL associations in a single-cell dataset.