Yahui Long

This paper introduces SpaFoundation, a histology foundation model that predicts spatial gene expression from tissue images, showing strong performance in prediction and transferability.

This paper introduces a hybrid network-based method (HyMSMK) to identify disease-related genes by incorporating multi-scale module kernels in biomolecule networks, showing improved performance over existing methods.

SpatialMap is a deep learning method for cell typing in subcellular spatial transcriptomics that outperforms existing methods and identifies novel cell types.

AiPROTAC predicts PROTAC degradation potential and designs an androgen receptor degrader, outperforming existing models and showing improved in vitro efficacy.

This study benchmarks machine learning methods for predicting synthetic lethality in cancer using matrix factorization and deep learning models, highlighting performance differences and emphasizing data and scenario importance.

SpatialGlue integrates spatial multi-omics data using a graph neural network to improve spatial domain detection and cross-modality correlation analysis.

GraphST improves spatial transcriptomics analysis by integrating clustering, integration, and deconvolution through graph self-supervised contrastive learning, achieving higher accuracy than existing methods.

This paper introduces GCATSL, a graph contextualized attention network for predicting synthetic lethality in human cancers, outperforming existing methods on multiple datasets.