Marzieh Ayati

Making Proteomics Accessible: RokaiXplorer for interactive analysis of phospho-proteomic data

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

Prediction of Kinase-Substrate Associations Using The Functional Landscape of Kinases and Phosphorylation Sites

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.

Constructing Functional Networks of Phosphorylation Sites Using Co-Phosphorylation

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

Robust Inference of Kinase Activity using Functional Networks

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.

Co-Phosphorylation Networks Reveal Subtype-Specific Signaling Modules in Breast Cancer

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: A Method for Comprehensive Kinase Substrate Annotation Using Co-phosphorylation Analysis

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.