AI-driven Q-learning for personalized acne genetics: Innovative approaches and potential genetic markers

Abstract

Genetic markers for acne are being studied to create personalized treatments based on an individual’s genes, and the field is benefiting from the application of artificial intelligence (AI) techniques. One such AI tool, the Q-learning algorithm, is increasingly being utilized by medical researchers to delve into the genetics of acne. In contrast to previous methods, our research introduces a Q-learning model that is adaptable to diverse sample groups. This innovative approach involves preprocessing data by identifying differentially expressed genes and constructing gene-gene connectivity networks. The key advantage of using the Q-learning model lies in its ability to transform acne gene data into Markovian domains, which are essential for selecting relevant genetic markers. Performance evaluations of our Q-learning model have shown high accuracy and specificity, although there may be some sensitivity variations. Notably, this research has identified specific genes, such as CD86, AGPAT3, TMPRSS11D, DSG3, TNFRSF1B, PI3, C5AR1, and KRT16, as being acne-related through biological verification and text data mining. These findings underscore the potential of AI-driven Q-learning models to revolutionize the study of acne genetics. In conclusion, our Q-learning model offers a promising approach for the selection of acne-related genetic markers, despite minor sensitivity fluctuations. This research highlights the transformative potential of Q-learning in advancing our understanding of the genetics underlying acne, paving the way for more personalized and effective treatments in the future.

Keywords

• Q-learning
• Genetic marker selection
• Acne genetics
• Reinforcement learning
• Gene expression data
• PubMed text data mining