Xuhua Yan

ERFMTDA: Predicting tsRNA-disease associations using an enhanced rotative factorization machine

ERFMTDA predicts tsRNA-disease associations by modeling complex feature interactions and using a biologically informed negative sampling strategy, outperforming existing methods.

Transferable spatial omics deconvolution with SpaRank

SpaRank is a transferable spatial deconvolution framework that uses rank-based encodings to robustly infer cell-type compositions across diverse biological contexts and omics modalities.

Mosaic integration of spatial multi-omics with SpaMosaic

SpaMosaic integrates spatial multi-omics data using contrastive learning and graph neural networks, improving spatial domain identification and missing modality imputation across diverse datasets.

ACE: a versatile contrastive learning framework for single-cell mosaic integration

ACE provides a contrastive learning framework for mosaic integration of single-cell multi-omics data, improving representation of cellular heterogeneities in datasets with incomplete modalities.

SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement

SSRE improves cell type identification in scRNA-seq by enhancing similarity measurement through sparse subspace representation and gene selection.