Xiao Xu

CleverXMBD1 improves cell abundance quantification by using a confidence-based multi-positive and unlabeled loss function, outperforming existing methods across multiple datasets.

The paper evaluates deconvolution methods for inferring immune infiltration from bulk gene expression using a DREAM Challenge, finding that some community-contributed methods, especially deep learning-based ones, perform well.

DAISM-DNNXMBD improves cell type proportion estimation in bulk RNA-seq by using in silico data augmentation and deep neural networks, outperforming existing methods on real-world datasets.

IMCellXMBD improves cell identification and quantification in imaging mass cytometry by denoising, distinguishing cells from noise, and normalizing protein expression.

This study evaluates seven scRNA-seq data processing pipelines across nine datasets from five platforms, comparing their performance in runtime, resource use, and data consistency.