Mark R. Chance

The paper introduces scProfiterole, a framework for clustering single-cell proteomic data using graph contrastive learning with spectral filters, showing improved cell type identification.

This paper identifies the structural basis of PU.1-BAF interaction and shows small molecules can disrupt it, offering a new therapeutic strategy for cancer.

This study analyzes phosphoproteome dysregulation in 5XFAD mice, identifying sex-specific and temporal patterns, novel biomarkers, and a key kinase pathway involved in Alzheimer's disease progression.

RokaiXplorer provides an intuitive web tool for phospho-proteomic data analysis and visualization, offering multiple functionalities and interactive features for researchers without specialized training.

The paper develops NetKSA, a network-based machine learning framework to predict kinase-substrate associations, outperforming existing methods and enabling annotation of phosphosites targeted by less-studied kinases.

The paper investigates co-phosphorylation across diseases, showing functionally related sites exhibit significant co-phosphorylation and can predict pathway or kinase associations.

This paper introduces RoKAI, a network-based framework that improves kinase activity inference by integrating functional information, showing enhanced accuracy and robustness in identifying dysregulated kinases in diseases.

The paper develops a network-based algorithm to subtype breast cancer using phosphorylation data, identifying subnetworks correlated with clinical subtypes and reproducible across datasets.

CoPhosK predicts kinase-substrate associations using co-phosphorylation analysis from mass spectrometry data, improving prediction performance and expanding the known landscape of kinase-substrate interactions.