ESHG Symposium “Machine Learning for Personalized Medicine”, Barcelona 2016
Machine learning is a powerful tool to analyze large, high-dimensional biomedical data sets (e.g. genetic, proteomic, or transcriptomic data). The objective is to find patterns in the deep sea of data that are significantly related to the development or progression of diseases or play a role in individual drug responses. Ultimately, machine learning algorithms are developed to provide physicians and other health professionals with clinical decision support.
At ESHG Symposium 2016 we bring together experts from this highly interdisciplinary field: human geneticists, bioinformaticians and machine learners will discuss recent research results and benefit from each others expertise.
Integrative and quantitative analysis of disease mutations in the RAS-RAF-MEK-ER...
Jul 18, 2016 1239 views
Development of methods for patient group stratification and tailored medical int...
Jul 18, 2016 1134 views
A network biology approach to epigenetic regulation
Jul 18, 2016 1148 views
Unlocking the potential of large prospective biobank cohorts for -omics data ana...
Jul 18, 2016 1386 views
Systems genetics with graphical Markov models
Jul 18, 2016 1125 views
Removing unwanted variation in machine learning for personalized medicine
Jul 18, 2016 1248 views
Identifying drug-targetable key drivers of disease
Jul 18, 2016 1173 views
