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

Yong Chi Chua, Hui Wen Nies, Izyan Izzati Kamsani, Haslina Hashim, Yusliza Yusoff, Weng Howe Chan, Muhammad Akmal Remli, Yong Hui Nies, Mohd Saberi Mohamad

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number100484
JournalEgyptian Informatics Journal
Publication statusPublished - Jun 2024


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

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Management Science and Operations Research


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