Bing Zhang

Analysis of isobaric quantitative proteomic data using TMT-Integrator and FragPipe computational platform

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: Covalent Inhibitor Binding Kinetics Profiling on the Proteome Scale

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

TabSyM: A Generative Pipeline for Small Multi-Cohort Omics Tabular Data

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

MetaSage: Machine Learning-Based Prioritization of Metabolic Regulators from Multi-Omics Data

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

Comprehensive evaluation of phosphoproteomic-based kinase activity inference

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

Patient-Derived Xenograft-Guided Prediction of Chemotherapy Response in Triple-Negative Breast 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.

Optimizing Neoadjuvant Treatment Response Prediction for Triple-Negative Breast Cancer Using Clinical Trial Data and Deep Auxiliary Learning

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

Deep learning prediction boosts phosphoproteomics-based discoveries through improved phosphopeptide identification

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

SEPepQuant enables comprehensive protein isoform characterization in shotgun proteomics

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

Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace

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

Chromatin potential identified by shared single cell profiling of RNA and chromatin

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.

Genetic dissection of femoral and tibial microarchitecture

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: an R package for integrative pathway analysis with modern PCA methodology and gene selection

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.