Molecular fingerprints of cancer - overview
Personalized medicine relies heavily on a patient’s data analysis, including but not limited to the genomic datasets that are becoming increasingly more available today. Taking this into consideration, prostate cancer will be taken as a case study to develop an interdisciplinary project that combines hypothesis-driven diagnostic strategies with data-driven estimations within a novel computational framework.
The aim of this project is to develop a novel computational framework to identify informative genomic variants in prostate cancer that addresses the urgent clinical need to stratify primary prostate cancer tissues into two classes: aggressive and insignificant disease. The computational framework will help to classify patients into discriminative groups and generate the associated genotypic profiles.