Jitao David Zhang

Comparison of Fixed Single Cell RNA-seq Methods to Enable Transcriptome Profiling of Neutrophils in Clinical Samples

This study compares single-cell RNA-seq methods for neutrophil transcriptome profiling in clinical samples, finding all methods produce high-quality data with 10X FLEX showing strong concordance with flow cytometry.

CellEKT: A robust chemical proteomics workflow to profile cellular target engagement of kinase inhibitors

CellEKT profiles cellular target engagement of kinase inhibitors using chemical proteomics to determine IC50 values across over 300 kinases and validate results with phosphoproteomics and NanoBRET.

A bottom-up approach identifies the antipsychotic and antineoplastic trifluoperazine and the ribose derivative deoxytubercidin as novel microglial phagocytosis inhibitors.

The paper identifies two compounds, trifluoperazine and deoxytubercidin, as novel microglial phagocytosis inhibitors using high-throughput screening and in vivo validation.

G-PLIP: Knowledge graph neural network for structure-free protein-ligand bioactivity prediction

The paper introduces a graph neural network for predicting protein-ligand interactions without structural data, achieving competitive performance using representation learning.

Kinex infers causal kinases from phosphoproteomics data

Kinex infers causal serine/threonine kinases from phosphoproteomics data using a workflow that scores kinase-substrate interactions and analyzes published and new datasets.

splicekit: a comprehensive toolkit for splicing analysis from short-read RNA-seq

splicekit provides tools for splicing analysis from short-read RNA-seq data, including alignment, alternative splicing detection, motif discovery, and visualization.

Gene symbol recognition with GeneOCR

GeneOCR is a web-based tool that recognizes, extracts, and checks gene symbols from figures, achieving over 80% accuracy in simulations.

Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research

Besca is a toolkit that streamlines scRNA-seq analysis with quality control, clustering, cell annotation, and bulk transcriptomics integration across datasets.

Tissue heterogeneity is prevalent in gene expression studies

The paper analyzes 2,692 gene expression datasets to show tissue heterogeneity affects 5-15% of samples, advocating for quality control methods to address this issue.

Assessment of network module identification across complex diseases

The paper evaluates module identification methods across disease-relevant networks using GWAS data to identify top-performing algorithms and disease-associated modules.

Gene-set enrichment with regularized regression

This paper introduces a regression-based approach for gene-set enrichment analysis that identifies sparse, interpretable gene-sets for genes of interest.

Comprehensive evaluation of computational cell-type quantification methods for immuno-oncology

The paper evaluates computational methods for estimating immune-cell composition from bulk RNA-seq data using simulated and real datasets, finding high accuracy for well-defined cell-type signatures.