Application of Machine Learning in Pharmacoproteomics

Pharmacoproteomics, which analyzes protein expressions influenced by pharmacological interventions, relies heavily on sophisticated data analysis. The integration of machine learning techniques with pharmacoproteomics highlights the potential to enhance drug and biomarker discovery and improve therapeutic outcomes. This chapter discusses fundamental aspects of machine learning methods and its crucial application in pharmacoproteomics. The various sources of proteomics data used for machine learning are discussed. Essential preprocessing steps such as filtering/smoothing, data alignment and peak matching, as well as identification and quantification are highlighted. Normalization, and feature selection, which are crucial for preparing pharmacoproteomic data for machine learning analysis, are covered. In addition, the chapter highlights machine learning algorithms used on large datasets typical in high-throughput proteomic studies with emphasis on supervised machine learning.