Siyu Hou

SVC: A Vision Transformer-based Spatially and Virtually Embedded Cell Model for Deciphering Subcellular Spatial Transcriptomic Heterogeneity

SVC predicts subcellular transcript localization and co-organization using a Vision Transformer model, revealing disease patterns and spatial biology not accessible at the cell level.

Accessible and reproducible deployment reveals the practical boundaries of single-cell foundation models

Across 13 single-cell foundation models and 94 datasets, PCA and established methods usually match or beat them; pretrained embeddings help mainly with scarce labels, rare cells and open-set detection.

FastCCC: A permutation-free framework for scalable, robust, and reference-based cell-cell communication analysis in single cell transcriptomics studies

FastCCC provides a permutation-free framework for reference-based cell-cell communication analysis, demonstrating robust performance across complex datasets and uncovering biologically relevant interactions.

Development of an orally bioavailable mSWI/SNF ATPase degrader and acquired mechanisms of resistance in prostate cancer

This paper develops an orally bioavailable PROTAC degrader of mSWI/SNF ATPases for prostate cancer and identifies resistance mechanisms through long-term treatment.

Development of attention-based robust deep learning model for tongue diagnosis by smartphone

The paper develops a deep learning model for smartphone-based tongue diagnosis, showing improved accuracy and robustness to environmental changes compared to baseline models.

St2cell: Reconstruction of in situ single-cell spatial transcriptomics by integrating high-resolution histological image.

St2cell reconstructs in situ single-cell spatial transcriptomics by integrating high-resolution histological images with deep learning and convex quadratic programming.