Wei Lan

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

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.

This paper introduces DSINMF, a deep matrix factorization method for clustering scRNA-seq data, which outperforms existing methods on eight datasets.

This paper introduces CDHGNN, a model using edge-weighted graph attention and heterogeneous graph neural networks to predict circRNA-disease associations, outperforming existing methods.