DNA损伤
人工智能
随机森林
氧化应激
计算机科学
机器学习
计算生物学
支持向量机
DNA
氧化损伤
生物
生物化学
遗传学
作者
Lazar M. Davidovic,Darko Laketić,Jelena Cumic,Elena Jordanova,Igor Pantić
标识
DOI:10.1016/j.cbi.2021.109533
摘要
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.
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