3 accepted papers
Clinical risk prediction plays a crucial role in early disease detection and personalized intervention. While recent models increasingly incorporate multimodal data, their development typically assumes access to large-scale, multimodal datasets and substantial computational resources. In practice, h…
Transcriptome foundation models (TFMs) hold great promises of deciphering the transcriptomic language that dictate diverse cell functions by self-supervised learning on large-scale single-cell gene expression data, and ultimately unraveling the complex mechanisms of human diseases. However, current…
Identification of cell subclasses using single-cell RNA-Sequencing (scRNA-Seq) data is of paramount importance since it uncovers the hidden biological processes within the cell population. While the nonnegative matrix factorization (NMF) model has been reported to be effective in various unsupervise…