Mariko Okada

The paper introduces AlloQuant, a computational tool that quantifies kinase regulation by analyzing structural ensembles, and demonstrates its application to CDK1 and CSK to reveal distinct conformational mechanisms of activation.

The paper develops a framework to assign context-dependent weights to gene regulations from literature, improving biological predictions and mathematical modeling of disease-specific networks.

This study shows how ErbB2 levels affect CDK4 inhibitor sensitivity and the timing of G1/S transition by altering c-Myc and cyclin D function in breast cancer cells.

The paper identifies a triple-deletion mutant of SRC kinase that alters activation loop dynamics, leading to changes in substrate preference and signaling outcomes.

This paper introduces an attention-based deep neural network to detect cis-regulatory elements at the single-cell level using multi-omics data, showing improved accuracy and cell state clustering.

The paper models pseudotime trajectories of single-cell transcriptomes in drug-treated breast cancer cells to identify key regulatory genes involved in drug resistance.

This study identifies THBS1 and FMOD as positive and negative regulators of the TGF-β1-SMAD4 axis in human skin aging, revealing two equilibrium states through mathematical modeling and omics analyses.

This study shows that sustained NFκB activity promotes cellular senescence by altering purine metabolism and epigenetic regulation through IκB and A20 dysfunction.

ASURAT performs unsupervised clustering and functional annotation of single-cell transcriptomes, improving biological interpretability with fewer manual curations.

The paper investigates NF-κB super-enhancers in B cells, finding they enhance transcriptional variability and gene expression through cis-regulatory interactions and NF-κB binding sites.