Application of Machine Learning Approaches in Electrophoresis—A Comprehensive Review

ABSTRACT

The electrophoretic technique is widely used to analyze and purify charged molecules based on their size, shape, and charge using an electric field for various applications, including DNA fingerprinting, genetic research, disease diagnosis, and drug development. However, electrophoretic data collection, processing, and analysis are hindered by experimental variability, instrument and imaging limitations, data processing difficulties, and analysis inconsistencies, all of which can distort band quality, impede accurate segmentation and quantification, and compromise reproducibility. Recent breakthroughs in data science have led to the development of powerful machine learning techniques, enabling models to learn from data, identify patterns, and make decisions or predictions. These techniques are being used to automate the collection, processing, and analysis of electrophoresis data. This review examines current applications of machine learning in electrophoresis, including the analysis and interpretation of electrophoresis‐derived data, prediction of electrophoretic properties and separation behavior, optimization of operating conditions, and integration with miniaturized sensing and point‐of‐care platforms. Cross‐cutting challenges related to dataset quality and independence, external validation, interpretability, computational requirements, reproducibility, and practical deployment are also discussed.