A major goal in computational biology is the development algorithms, analysis techniques, and tools towards deep mechanistic understanding of life at a molecular level. In the process, computational biology must take advantage of the new developments in artificial intelligence and machine learning, and then move beyond pattern analysis to provide testable hypotheses for experimental scientists. This talk will focus on our contributions to this process and the relevant related work. We will first discuss the development of machine learning techniques for partially observable domains such as molecular biology; in particular, methods for accurate estimation of frequency of occurrence of hard-to-measure and rare events. We will then show how these methods play key roles in inferring protein function and the phenotypic effect of coding sequence variants, with an emphasis on understanding the molecular mechanisms of human genetic disease. We will assess the value of these methods in a wet lab where we tested the molecular mechanisms behind selected de novo mutations in a cohort of individuals with neurodevelopmental disorders. We finally discuss implications for genome interpretation.