Li Ding

This study identifies CXCL-CXCR2 signaling as critical for SCLC liver metastasis by enhancing cancer cell migration and interactions with endothelial cells.

This paper introduces MOBA-seq, a platform to map genetic regulators of metastasis at single-colony resolution, identifying tissue-specific suppressors and universal essential genes.

The paper characterizes immune microenvironment dysregulation in multiple myeloma using single-cell data and links it to disease progression and patient outcomes.

This study identifies LINC01432 as a lncRNA that binds to CELF2 and inhibits apoptosis in multiple myeloma, contributing to short progression-free survival to standard therapy.

This study identifies KAT2A as a therapeutic target in a subset of well-differentiated, microsatellite-stable colorectal cancers by showing its dependency and functional consequences.

DeST-OT aligns spatiotemporal transcriptomics data from developmental timepoints using optimal transport to model cell growth, death, and differentiation.

This study identifies breast cancer subtypes and their lineage origins by analyzing chromatin accessibility and transcriptional dynamics using multi-omic techniques.

CalicoST infers allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics data, achieving higher accuracy than existing methods.

SC2MeNetDrug is a tool that identifies cell-cell signaling targets and predicts drugs using scRNA-seq data to analyze disease microenvironments.

SCMER identifies non-redundant molecular features that delineate both common cell lineages and rare cellular states in single-cell data.

The paper introduces HotPho, a tool that identifies 3D co-clustering of phosphosites and cancer mutations on protein structures, revealing 474 hybrid clusters with potential functional significance.

The paper introduces MEDALT and LSA to reconstruct copy number lineage trees from single-cell data and identify fitness-associated genes, showing improved accuracy over phylogenetics methods in cancer datasets.

The paper identifies ancestry-specific germline variants in cancer predisposition genes and their somatic effects across diverse populations.

This study identifies a large number of structural variants in human genomes using multiple sequencing technologies and presents them as a gold standard for future studies.

The paper develops ADmiRE to annotate miRNA variants in large population datasets, identifies highly constrained miRNAs, and provides conservation scores for future disease studies.