promor

promor performs label-free quantification proteomics data analysis and builds machine-learning-based predictive models to prioritize top protein candidates.

Key Features:

• R package: Implemented in R for computational proteomics analyses.
• Label-free quantification: Performs label-free quantification of proteins directly from mass spectrometry data.
• Predictive modeling: Incorporates machine learning methodologies to build predictive models for protein candidate ranking.
• High-throughput analysis: Supports workflows applicable to high-throughput proteomic studies.

Scientific Applications:

• Biomarker discovery: Prioritizes protein candidates for identification of biomarkers in biological processes and disease mechanisms.
• Proteome quantification: Enables quantitative analysis of proteomes using label-free mass spectrometry data.
• Predictive proteomics: Produces predictive models from proteomics datasets to assess protein relevance.

Methodology:

Performs label-free quantification from mass spectrometry data and applies machine-learning-based predictive modeling.