Artificial intelligence approaches to the biochemistry of oxidative stress: Current state of the art

Artificial intelligence (AI) and machine learning models are today frequently used for classification and prediction of various biochemical processes and phenomena. In recent years, numerous research efforts have been focused on developing such models for assessment, categorization, and prediction of oxidative stress. Supervised machine learning can successfully automate the process of evaluation and quantification of oxidative damage in biological samples, as well as extract useful data from the abundance of experimental results. In this concise review, we cover the possible applications of neural networks, decision trees and regression analysis as three common strategies in machine learning. We also review recent works on the various weaknesses and limitations of artificial intelligence in biochemistry and related scientific areas. Finally, we discuss future innovative approaches on the ways how AI can contribute to the automation of oxidative stress measurement and diagnosis of diseases associated with oxidative damage.

Automated summary

Artificial intelligence and machine learning models are widely used for classification and prediction of biochemical processes. Recent research efforts have focused on developing models for assessing and predicting oxidative stress, using supervised machine learning to automate the evaluation and quantification of oxidative damage in biological samples. This review covers the applications of neural networks, decision trees, and regression analysis as common strategies in machine learning, and reviews the weaknesses and limitations of AI in biochemistry and related fields. In addition, future innovative approaches for AI to contribute to the automation of oxidative stress measurement and diagnosis of diseases associated with oxidative damage are discussed.