Machine Learning-Derived Metrics Enable Computational Method Comparison in Phosphoproteomics Research
Protein phosphorylation dysregulation has been recognized as a key feature of several diseases, especially cancer. In recent years, phosphoproteomic research has revealed novel, effective biomarkers and drug targets for disease prognosis and treatment. Tandem mass spectrometry (MS/MS)-based phosphoproteomics provides a high-throughput method to study protein phosphorylation in complex biological samples. However, translating phosphoproteomic data into relevant biological and clinical insights relies on effective data analysis.