Lijun Cheng

This study uses CRISPR-Cas9 and RNAseq to identify novel synthetic lethal targets in TNBC cells resistant to cisplatin/doxorubicin, highlighting potential drug targets for combination therapies.

The paper identifies eQTLs, rQTLs, and pQTLs in the human brain and shows that many eQTL effects are attenuated post-transcriptionally, yet still correlate with schizophrenia GWAS signals.

The paper develops a biclustering regression model to identify key genes and their relationships with clinical phenotypes in severe preeclampsia, recommending 81 potential druggable targets.

The paper identifies 46,802 enhancers and 164 psychiatric disorder variants with regulatory effects in the developing cortex and organoids using massively parallel reporter assays and deep learning.

The paper processes single-nuclei multi-omics data from 388 human brains to identify regulatory elements and build cell-type networks, revealing changes in aging and neuropsychiatric disorders.

The paper generates a molecular neuroanatomical map of the human prefrontal cortex using spatial transcriptomics and single nucleus RNA-sequencing, revealing novel spatial domains and cell-cell interactions linked to psychiatric disorders.

The paper identifies and validates enhancer regions in primary human neural progenitor cells using capture STARR-seq, linking them to genes involved in psychiatric disease pathways.

DSCN selects effective target combinations by integrating CRISPR screening data and protein-protein interaction networks, showing high accuracy and speed compared to existing methods.

DGCyTOF improves cell type identification in single-cell mass cytometry data using deep learning and visualization, achieving high accuracy and intuitive clustering.

The paper presents a large-scale cancer pharmacogenomic screen with 11,576 experiments on 910 drug combinations across 85 cancer cell lines, enabling crowd-sourced prediction of synergistic drug pairs and biomarkers.