Motivation

In the domain of health sciences, the time and cost of sequencing genomes has been reduced by a factor of one million in less than ten years. Nowadays, personal genomes can be sequenced and mapped for a few thousand dollars. Therefore, Personal Omics can be considered as a key enabler for predictive medicine, where a patient’s genetic and metabolic profiles can be used to determine the most appropriate medical treatment. Data-driven medicine is coming to the forefront of medical research by addressing new challenges, among which the statistical handling of ultra-large data sets. The Data Science Lab contributes in addressing some of these challenges by developing machine learning algorithms to extract information out of complex biological data.

Publications

• Guerrier, S., Mili, N., Molinari, R., Orso S., Avella-Medina, M. & Ma, Y.,“A Paradigmatic Regression Algorithm for Gene Selection Problems”. Frontiers in Genetics, Statistical Genetics and Methodology, 2016. Link Full text

Conference Proceedings