Technological advances in life science, such as the development of DNA chips or massively parralel sequencing technologies, promises important progress in life science and medicine including the understanding of basic biological processes, the identification of new drug targets and new drugs, and the precise profiling of patients to reach a more personalized medicine. However to achieve these goals we must be able to process and analyse the huge amount of data generated by the new technologies. In this lecture I briefly introduce the machine learning approach to this challenge, including the popular support vector machine (SVM) algorithm, and illustrate the power of machine learning on several applications in cancer informatics and chemoinformatics.