Application of artificial intelligence for detection of chemico-biological interactions associated with oxidative stress and DNA damage

In recent years, various AI-based methods have been developed in order to uncover chemico-biological interactions associated with DNA damage and oxidative stress. Various decision trees, bayesian networks, random forests, logistic regression models, support vector machines as well as deep learning tools, have great potential in the area of molecular biology and toxicology, and it is estimated that in the future, they will greatly contribute to our understanding of molecular and cellular mechanisms associated with DNA damage and repair. In this concise review, we discuss recent attempts to build machine learning tools for assessment of radiation - induced DNA damage as well as algorithms that can analyze the data from the most frequently used DNA damage assays in molecular biology. We also review recent works on the detection of antioxidant proteins with machine learning, and the use of AI-related methods for prediction and evaluation of noncoding DNA sequences. Finally, we discuss previously published research on the potential application of machine learning tools in aging research.

Automated summary

In recent years, various AI-based methods have been developed to uncover chemico-biological interactions associated with DNA damage and oxidative stress in the fields of molecular biology and toxicology. Research has focused on developing machine learning tools for assessing radiation-induced DNA damage and noncoding DNA sequence prediction and evaluation.