فهرست نشریات علمی دسترسی آزاد ایران

Dermatology is undergoing a profound transformation driven by rapid advances in bioinformatics and high-throughput omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and metagenomics. These integrative approaches have provided unprecedented insights into the molecular basis of skin health and diseases such as melanoma, psoriasis, atopic dermatitis, acne vulgaris, and autoimmune disorders. This review highlights how bioinformatics tools and machine learning algorithms facilitate the analysis of complex, multidimensional datasets, enabling disease gene discovery, pathway analysis, biomarker identification, and predictive modeling. Machine learning and artificial intelligence (AI) increasingly enhance dermatological imaging, clinical decision support, and teledermatology, improving diagnostic accuracy and enabling personalized care. Multi-omics integration offers a systems-level understanding of disease mechanisms and supports precision medicine by stratifying patients based on molecular profiles and tailoring treatments. Despite these advances, challenges remain, including standardizing data formats, integrating heterogeneous datasets, ensuring reproducibility, maintaining data privacy, and translating findings into clinical practice. Additionally, the underrepresentation of diverse populations, computational limitations, and regulatory hurdles impede widespread adoption. This review discusses recent bioinformatics tools, methodologies, and applications developed up to 2025, emphasizing the transformative potential of multi-omics integration and AI-powered analytics. It also addresses emerging trends, technical and ethical considerations, and future directions for precision dermatology. The convergence of computational biology and clinical dermatology is expected to shift the field toward personalized, predictive, and preventive skin healthcare, requiring interdisciplinary collaboration, inclusive research, and accessible platforms to bridge omics data and clinical practice, ultimately improving patient outcomes and redefining dermatological care standards.