The current scenario is characterized by a technical capacity to produce large-scale data that goes beyond our analytical capacity for interpretation. The comprehensive characterization of genomic, epigenomic, transcriptomic and proteomic alterations in pathological states, especially when correlated with clinical characteristics, has a great potential to improve the diagnosis and prognosis of diseases, and especially, to enable the practice of personalized medicine in the reality of care activities. This project comprises four big areas: Computational Sciences, Molecular Biology, Bioinformatics and Health Sciences. The aim of this project is to contribute to the personalized medicine seeking, through the development of computational methods and strategies in a Bioinformatic context, to improve the creation of preventive measures for cancer, as well as to advance in diagnosis and prognosis precision of the disease. The main research question that leads this project is: "Will the development and integration of meta-heuristics, machine learning and biological annotation data deliver high-quality solutions for the identification of cancer biomarkers?"