Roger R. Reddel

OnCorr analyzes mRNA-protein correlations across pan-cancer datasets to improve precision oncology by providing a web tool for integrating transcriptomics data with protein information.

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

This study identifies STC1 as a pressure-induced gene in IPAH PASMCs that suppresses proliferation and vascular remodeling through PIEZO1, suggesting STC1 as a therapeutic target.

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

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

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.

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.

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

The paper introduces a rapid, high-throughput proteomics sample processing protocol called Heat n Beat, achieving consistent peptide yield and quality across diverse tissue types in under an hour.

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.