There is great potential for machine learning to contribute to our understanding of complex human diseases and clinical decision making. Rapidly evolving biotechnologies are making it progressively easier to collect multiple and diverse genome-scale datasets to address clinical and biological questions. As machine learners we have to use our modeling skills responsibly. I will talk about several of our contributions to answering various questions using predictive models. First, I will talk about inferring per-gene regulation for >10,000 genes in 21 cancer tissues with implications for global regulation patterns as well as interpreting biomarkers in specific tissues. I will then share a model we built to predict whether a child with a TP53 mutation is likely to get cancer before the age of 6 using methylation data. Here, biological interpretation of the predictors is less straight-forward due to the genome-wide nature of the predictive signal. Finally, I will talk about deep learning models to predict drug response in cancer cell lines as well as our attempts to translate these findings to patients.