ProteoBoostR: an interactive framework for supervised machine learning in clinical proteomics.

ABSTRACT:

Background

Mass spectrometry-based proteomics enables high-throughput quantification of thousands of proteins in clinical samples, fueling biomarker discovery for disease diagnosis and prognosis. However, leveraging complex proteomic profiles for predictive modeling often requires advanced machine learning (ML) expertise that many biomedical researchers lack. User-friendly tools are needed to apply state-of-the-art ML algorithms to proteomics data. XGBoost is a powerful tree-based ML algorithm known for high accuracy in classification tasks, and has been successfully used to classify cancer subtypes from multi-omics data.

Methods

We developed ProteoBoostR, a Shiny application that streamlines supervised ML on protein abundance datasets. It allows researchers to train, evalua

SUBMITTER: Topitsch A 

PROVIDER: S-EPMC12849323 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature