Jiaxing Chen

The paper introduces CSCN, a method to infer cell-specific causal gene regulatory networks from scRNA-seq data, reducing false positives and improving state clustering.

stTrace detects spatial-temporal domains in spatial transcriptomics data to trace developmental paths, showing better alignment with H&E images and compactness in developmental levels.

dynDeepDRIM uses deep learning to predict direct regulatory interactions from single-cell time-course gene expression data, outperforming existing methods.

This study uses simulation and sequencing data to show that multiple SARS-CoV-2 variants can coexist in COVID-19 patients, with an increase in the number of coinfected variants over time.

DeepDRIM is a deep neural network that reconstructs cell-type-specific gene regulatory networks from single-cell RNA-seq data, outperforming existing methods and distinguishing functional modules in COVID-19 patients.

This paper introduces a new distance metric, dr, that satisfies the triangular inequality for gene expression profiles and shows it provides more robust clustering than the absolute correlation distance.