Phillip James Robinson

The paper generates an organ-resolved phosphoproteome map from FFPE rat tissues, showing it preserves signaling biology and enables kinase activity inference.

This study uses forebrain organoids to show that schizophrenia involves sex-specific protein-state dysregulation, with changes in post-translational modifications and proteomic pathways rather than transcript levels.

This study uses patient-derived organoids to identify synaptic protein and phospho-regulatory deficits in schizophrenia.

This study develops a centrifugation protocol to isolate growth cones and immature synapses from human organoids, capturing synaptic development stages and enabling models for studying synaptic function and dysfunction.

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.

This study identifies a phospho-regulated interaction between Dyn1xA and Endophilin A1 via a unique C-terminal extension, essential for ultrafast endocytosis.

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.