In the coming years, human genome research will likely transform medical practices. Genome-wide association studies (GWAS) are an example of the research effort made to allowing scientists to identify genes involved in human disease, reaction to treatments or symptom severity. Indeed, the unique genetic profile of an individual and the knowledge of molecular basis of diseases are leading to the development of personalized medicines and therapies, but the exponential growth of available genomic data requires a computational effort that may limit the progress of personalized medicine. Within this context, we propose the development of a novel hardware and software integrated system, named HUGenomics. The framework aims at becoming an advanced support for personalized medicine research. Thanks to more efficient algorithms and data integration from different biological sources, HUGenomics aims at simplifying the interpretation of biological information and facilitating genomic research process by means of both computational and data visualization tools.
|Titolo:||HUGenomics: A support to personalized medicine research|
|Data di pubblicazione:||2017|
|Appare nelle tipologie:||04.1 Contributo in Atti di convegno|