Proteomics and machine learning in the prediction and explanation of low pectoralis muscle area.

ABSTRACT: Low muscle mass is associated with numerous adverse outcomes independent of other associated comorbid diseases. We aimed to predict and understand an individual's risk for developing low muscle mass using proteomics and machine learning. We identified eight biomarkers associated with low pectoralis muscle area (PMA). We built three random forest classification models that used either clinical measures, feature selected biomarkers, or both to predict development of low PMA. The area under the receiver operating characteristic curve for each model was: clinical-only = 0.646, biomarker-only = 0.740, and combined = 0.744. We displayed the heterogenetic nature of an individual's risk for developing low PMA and identified two distinct subtypes of participants who developed low PMA. While additio

SUBMITTER: Enzer NA 

PROVIDER: S-EPMC11297919 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature