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

• Published in: Chemico-biological interactions 2022-05, Vol.358, p.109888, Article 109888
• Main Authors: Pantic, Igor, Paunovic, Jovana, Pejic, Snezana, Drakulic, Dunja, Todorovic, Ana, Stankovic, Sanja, Vucevic, Danijela, Cumic, Jelena, Radosavljevic, Tatjana
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Machine Learning; Neural Networks, Computer; Oxidative damage; Oxidative Stress; Reactive oxygen species; Signal analysis; Toxicity
• ISSN: 0009-2797; 1872-7786
• DOI: 10.1016/j.cbi.2022.109888

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. •Artificial intelligence can be used for classification and prediction of oxidative stress.•Some of the AI approaches include neural networks, decision trees and regression analysis.•AI-based methods can be used to automate decision processes in biochemistry research.