Machine Learning and Omics for Biomarker Discovery in Human Skin Diseases: A Scoping Review
Ana Duarte, Orlando BeloIntroduction:
The synergistic relationship between Artificial Intelligence and Medicine has the potential to revolutionize our understanding of how genomics, transcriptomics, and other omics influence susceptibility to specific diseases. Finding the most appropriate biomarkers from omics is still a challenge for most diseases, but it can bring tremendous benefits for disease diagnosis, prognosis, and treatment.
Methods:
This scoping review provides a comprehensive analysis of all existing research on omics biomarkers for skin conditions discovered by machine learning algorithms, following the Joanna Briggs Institute methodology.
Results:
After screening, 73 papers met the eligibility criteria. These articles correspond to studies conducted mainly in China (49.3%) and the United States (20.5%), and most are relatively recent (2020–2022). The studies mainly focus on cancers and systemic lupus erythematosus, as well as the discovery of diagnostic (49.3%) and prognostic biomarkers (45.2%). Only 11.0% of the publications explore predictive biomarkers. LASSO, random forest, and support vector machines are among the top three most frequently employed machine learning algorithms. Finally, transcriptomic data (80.8%) are the most commonly used type of omics, and HLA-DQB1 and IFI27 emerged as promising biomarkers.
Discussion:
The findings highlight the growing number of machine learning-based omics studies in dermatology. However, key gaps remain, including geographic concentration and limited exploration of non-cancer dermatologic conditions.
Conclusions:
This scoping review enabled the identification and comparison of potential biomarkers associated with skin diseases discovered through machine learning techniques. Despite their promising role in disease management and advancing precision medicine, such studies remain scarce in dermatology.