In the last decades, the decrease in the cost of next-generation sequencing (NGS) technologies has allowed the widespread of many omics data (e.g., transcriptomics, genomics). This thesis focuses on a multi-omics approach to cluster patients so that similar ones are assigned to the same cluster, simultaneously considering all data sources. The proposed method has been evaluated on patients affected by myeloid and lymphoid leukemias (AML, ALL). The method considers two types of transcriptomics data, miRNA and mRNA expression. The expression measures the quantity of the molecule in the sample, which is crucial in regulating transcriptional and post-transcriptional processes. Many techniques based on multi-omics clustering of samples are presented.