Genomic Data Platform
Dedicated to Skincare and Cosmetics R&D
What is
GenesisGaia Beauty?
Genomic Platform for Skincare & Cosmetics
GenesisGaia Beauty is a feature dedicated to skincare and cosmetics R&D and product development.
Taking the guesswork out of the skincare equation, GenesisGaia Beauty supports researchers in identifying molecular and genomic correlations between cosmetic ingredients, genes, and their effect(s) on the skin.
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FEATURE OVERVIEW 1
Network Graph to
Visualize the Relationship
Predicting Correlations Between Skincare/Cosmetics Ingredients and Skin Genes.
Based on data generated from cosmetic ingredients and epithelial cells with RNA sequencing, GenesisGaia Beauty empowers researchers by visualizing the predicted correlation(s) between skincare/cosmetics ingredients and skin genes.
Network Graph indicates positive and negative correlation(s) as well as their strengths, to provide an intuitive tool for researchers to identify associations, relationships, and potential effects of cosmetic ingredients and genes on the skin.
FEATURE OVERVIEW 2
Access to Genomic Skincare & Cosmetic R&D Database
Trust the Science
With the largest genomic R&D database for the Japanese population, GenesisGaia Beauty features an extensive range of genomic and phenotypic data sets relevant to skincare and cosmetics R&D.
With over 340,000 curated microarray genomes, researchers can select relevant genes to validate their R&D initiatives and tailor new products specifically to the Japanese population. The range of dataset is extensive, covering multiple genes linked to skin aging, pigmentation, formation of wrinkles, freckles or spots, effects of UV or antioxidants, atopic dermatitis, and much more.
Offering case and control options to researchers, GenesisGaia Beauty supports the development of customized cohort studies through opt-in and re-contactable participants.
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FEATURE OVERVIEW 3
Platform Customization
Tailored for You
GenesisGaia Beauty offers modular services to customize its platform to the specific needs and requirements of researchers, including the addition of new data sets, import of own and third-party databases as well as tailored analytical services, algorithms, A.I. and machine learning-based models, and more.