A Machine Learning Classifier for Assigning Individual Patients With Systemic Sclerosis to Intrinsic Molecular Subsets

Abstract

We developed a highly accurate classifier for SSc molecular subsets for individual patient samples. The method can be used in SSc clinical trials to identify an intrinsic subset on individual samples. Our method provides a robust data-driven approach to aid clinical decision-making and interpretation of heterogeneous molecular information in SSc patients.