The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. I will describe how to combine symbolic and statistical Artificial Intelligence approaches for the analysis of biological and biomedical data. I will focus on the identification of gene-disease associations, interpretation of causative variants, and prediction of protein functions.