Mass spectrometry combined with machine learning identifies novel protein signatures as demonstrated with multisystem inflammatory syndrome in children.

ABSTRACT: Rapid and accurate diagnosis of emerging inflammatory illnesses is challenging due to overlapping clinical features with existing conditions. We demonstrate an approach that integrates proteomic analysis with machine learning to identify diagnostic protein signatures, using the example of SARS-CoV-2-induced multisystem inflammatory syndrome in children (MIS-C). We used plasma samples collected from subjects diagnosed with MIS-C and compared them first to controls with asymptomatic/mild SARS-CoV-2 infection and then to controls with pneumonia or Kawasaki disease. We used mass spectrometry to identify proteins and support vector machine (SVM) algorithm-based classification schemes to identify protein signatures. Diagnostic accuracy was assessed by calculating sensitivity, specificity, and ar

SUBMITTER: Guzman Rivera J 

PROVIDER: S-EPMC12546946 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature