Tumor heterogeneity plays a critical role in cancer progression and therapy resistance. Not only intertumoral heterogeneity leads to the definition of distinct tumor subtypes, but also intratumoral heterogeneity shows at distinct cell clones with different selective advantages. Emerging biomedical technologies, in particular, those generating omics data (e.g., genomics, transcriptomics, proteomics) now make it possible to ask which molecular entities govern tumor heterogeneity and can be candidates for disease biomarkers and therapeutical targets. Omics data are high-dimensional, with the number of features greatly outnumbering the number of observations. This calls for the need to develop statistical and machine learning methods able to translate vast amounts of data into meaningful biological solutions. Learning high-dimensional ‘omic data poses many challenges, in particular for parameter estimation and generation of interpretable solutions. In this talk, I will cover strategies for unveiling relevant information from high-dimensional omic data, including model regularization for feature selection and network-based modeling, with examples of application in the cancer research domain.