In modern scientific research, data are often collected from multiple modalities. Since different modalities could provide complementary information, statistical prediction methods using multi-modality data could deliver better prediction performance than using single modality data. However, one special challenge for using multi-modality data is related to block-missing data. In this talk, I will introduce several supervised learning methods for block-missing multi-modality data, including the direct sparse regression procedure using covariance from multi-modality data for the regression problem and the weighted nearest neighbor classifier for the classification problem. The effectiveness of the proposed methods is demonstrated by theoretical studies, simulated examples, and a real application from the Alzheimer's Disease Neuroimaging Initiative. The comparison between our proposed methods and some existing methods also indicates the advantages of our methods.