The aim of this thesis is to exploit microRNA isoforms expression profiles and Artificial Intelligence (AI) tools to classify samples from different cancer studies. MicroRNA (miRNA) are small non-coding RNA molecules of 19-22 nucleotides that regulate gene expression via base-pairing with complementary sequences within mRNA molecules. Each miRNA sequence can occur with some modifications that may influence the final behavior of the molecule, this sequence is called isoform. Thanks to the evolution of sequencing technologies, an increasing number of miRNA expression data were released. The Cancer Genome Atlas (TCGA) is one of the projects that collect these kinds of data. Studies carried out on tumor and healthy samples showed differential expression of miRNA between the two categories, in particular for those miRNA families related to oncogenic or tumor suppressors gene pathways.