Lingxi Chen

This study shows that inhibiting Notch signaling and forcing cell cycle re-entry in Müller glia promotes their reprogramming into neurons in uninjured mouse retina.

This paper introduces InvGraphCox, a framework that improves survival prediction by learning invariant graph representations under distribution shifts, outperforming baselines in discrimination and risk stratification.

BIND identifies biological significance of missing values in proteomics data, improving analysis and biomarker discovery.

CRISGI identifies critical transient gene interactions from bulk RNA-seq data to predict early respiratory virus infections and their onset timing across multiple viruses.

GREA improves gene set enrichment analysis by incorporating gene interactions, outperforming existing tools in detecting pathways related to respiratory viral infections.

This paper introduces TCRanno, a tool to quantify TCR repertoire specificity, revealing insights into infections, autoimmunity, and cancer through analysis of 4,195 repertoires.

The paper introduces SEAT, a method that incorporates cell hierarchy to analyze functional diversity in single-cell data, improving clustering accuracy and revealing insights into cell signaling and regulatory networks.

SMURF embeds scRNA-seq data using matrix factorization with a Poisson-Gamma divergence objective, preserving self-consistency and recovering gene expression and cell cycle time courses.

STORM reconstructs single-cell resolution quasi-structures from spatial transcriptomes by reducing pseudo affinities and embedding interactions into a low-dimensional space.

SCYN is a single-cell CNV segmentation method using dynamic programming, showing high accuracy and speed on scDNA datasets.

scSVAS provides an online platform for real-time interactive visualization of single-cell copy number variation data, enabling efficient analysis and sharing of large-scale genomics results.

CNAHap phases germline variants using tumor allele-specific copy number alterations, achieving accurate haplotyping and long phasing blocks in Hepatocellular carcinoma.

SpecHap is a diploid phasing algorithm using spectral graph theory, showing efficiency and accuracy across multiple sequencing technologies and large genomes.

I-Impute is a self-consistent imputation method for scRNA-seq data that outperforms existing tools in correlation and clustering accuracy on both simulated and real datasets.

DeepMF improves subtype discovery and data recovery in cancer omics data by handling noise and missing values with a deep learning-based matrix factorization model.