About
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.
Videos

Removing unwanted variation in machine learning for personalized medicine
Jul 18, 2016
·
1230 views

Integrative and quantitative analysis of disease mutations in the RAS-RAF-MEK-ER...
Jul 18, 2016
·
1220 views

Identifying drug-targetable key drivers of disease
Jul 18, 2016
·
1151 views

Unlocking the potential of large prospective biobank cohorts for -omics data ana...
Jul 18, 2016
·
1363 views

A network biology approach to epigenetic regulation
Jul 18, 2016
·
1132 views

Development of methods for patient group stratification and tailored medical int...
Jul 18, 2016
·
1118 views

Systems genetics with graphical Markov models
Jul 18, 2016
·
1114 views