Data availability is important when researchers want to apply artificial intelligence algorithms to extract biomarkers and generate predictive models for disease diagnosis, response to treatment and prognosis. For cutaneous melanoma clinical, biological and imaging data are scattered through the web. ebioMelDB is the first database to integrate the widest collections of RNA-Seq gene expression and clinical data with clinical and dermoscopy images, all manually curated and organized in categories. ebioMelDB aspires also to host our under development predictive models in cutaneous melanoma diagnosis, response to treatment and prognosis based on combinations of the different data types hosted. As a first step towards this direction, we apply an ensemble dimensionality reduction technique employing a multi-objective optimization heuristic algorithm that finds the best feature subset, the best classifier among linear SVM, Radial Basis Function Kernel SVM and random forest and their optimal parameters to predict the vital status of patients in different time windows based on a large cohort of patients’ gene expression data. The results are very encouraging in performance metrics compared with state-of-the-art algorithms. The database is available at http://www.med.upatras.gr/ebioMelDB.