Bing Zhang

The paper introduces TMT-Integrator, a tool for analyzing isobaric proteomic data, showing improved performance in protein and phosphorylation site quantification compared to existing tools.

COOKIE-Pro quantifies covalent inhibitor binding kinetics across the proteome, revealing off-targets and kinetic parameters for BTK inhibitors.

TabSyM improves predictive modeling of small, high-dimensional biomedical datasets by generating synthetic data and aligning cross-cohort distributions.

MetaSage identifies metabolic regulators using machine learning across 15 cancer cohorts, finding genes and metabolites have comparable regulatory influence.

This paper evaluates kinase activity inference methods using phosphoproteomics data and benchmarking approaches, finding that curated kinase-substrate libraries significantly impact results.

The paper constructs a co-regulation map of phosphosites using machine learning and phosphoproteomic data, revealing kinase-substrate associations and identifying potential therapeutic targets in cancer.

This paper develops chemotherapy-specific predictors for triple-negative breast cancer using patient-derived xenografts and multi-omic data, achieving high AUROC scores for response prediction.

This study develops a deep auxiliary learning approach to predict TNBC treatment response using clinical trial data, showing improved performance over traditional methods.

The paper shows that ZNRF3/RNF43 ubiquitinates and degrades EGFR, and their loss increases EGFR signaling, promoting cancer growth.

DeepRescore2 improves phosphopeptide identification using deep learning, leading to more phosphosites detected and new biological insights in liver cancer.

SEPepQuant improves protein isoform characterization in shotgun proteomics by addressing limitations in existing methods, identifying isoform-level regulation events, and enabling cross-study comparisons.

scSpace reconstructs a pseudo-space from single-cell RNA-seq data to identify spatially variable cell subclusters and their biological relevance.

The paper introduces SHARE-seq, a method to profile RNA and chromatin in single cells, and identifies DORCs that predict lineage commitment by chromatin accessibility changes.

The paper uses high-resolution imaging and QTL analysis to study genetic control of bone microarchitecture in mice, identifying 50 candidate genes with potential roles in bone biology.

pathwayPCA is an R package that performs integrative pathway analysis with modern PCA methods and gene selection, enabling multi-omics data analysis and sample-specific pathway activity estimation.