• bioinformatics
• high-dimensional data clustering
• cancer data analysis
• omics data analysis
• biclustering

Recent advancements in methodologies for extracting omics data opened up new perspectives in the study of different types of oncologic diseases. The exploitation of massive cancer omics data requires proper data mining tools and algorithms, able to address all the challenges posed by their high dimensionality and the presence of high noise levels. The computational experiments carried out in this thesis work focus on the use of biclustering techniques, and aim to propose a possible workflow for a proper use of biclustering over high-dimensional omics data.