Qing Zhong

Machine Learning-Driven Discovery of Synergistic Protein Interactions Identifies ATL3 as a Putative Biomarker for Cancer Drug Response

The paper introduces synerOmics, a machine learning framework to identify synergistic protein interactions, finding ATL3 as a biomarker for lapatinib resistance in breast cancer.

A proteogenomic approach to identifying a gene signature associated with HOXB13 G84E carrier status in prostate cancer tumours

The paper identifies a gene and protein expression signature associated with HOXB13 G84E carrier status in prostate cancer using proteogenomic analysis.

Federated deep learning enables cancer subtyping by proteomics

ProCanFDL uses federated deep learning to subtype cancers using proteomic data while preserving privacy, achieving performance gains over local models.

Blocking MIF secretion enhances CAR T-cell efficacy against neuroblastoma

This study identifies MIF as a key immunosuppressive factor in neuroblastoma that inhibits CAR T-cell efficacy, showing that blocking MIF enhances T-cell activation and therapeutic potential.

Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning

The paper introduces a deep learning model to augment cancer cell line multi-omic data, increasing the number of profiles and uncovering drug resistance mechanisms.

Transformer-based deep learning integrates multi-omic data with cancer pathways

This paper introduces DeePathNet, a Transformer-based model that integrates cancer pathways with multi-omic data to improve drug response prediction and cancer classification.

Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities

The paper presents a pan-cancer proteomic map of 949 cell lines, revealing protein-specific biomarkers of cancer vulnerabilities and showing proteomic data's superior predictive power for drug response.

Convergent network effects along the axis of gene expression during prostate cancer progression

The paper investigates how genomic alterations affect molecular networks in prostate cancer by integrating genomic, transcriptomic, and proteomic data from 105 samples, identifying a sub-network predictive of tumor aggressiveness and recurrence.

Rapid proteotyping reveals cancer biology and drug response determinants in the NCI-60 cells

This study rapidly generates proteomic data for NCI-60 cancer cell lines and identifies drug response determinants by integrating proteomic, genetic, and transcriptomic data.

Multi-region proteome analysis quantifies spatial heterogeneity of prostate tissue biomarkers

This study quantifies spatial proteome heterogeneity in prostate tissues using multi-region analysis and identifies biomarker variability across benign and malignant tissues.