In this thesis we present bioinformatic tools and algorithms for the analysis of genomic data such as those generated by microarray devices or next generation sequencing techniques. Particularly, we develop new approaches to gene set analysis. The described procedures should be useful in practice to tackle complex biological experiments, but hopefully will also be methodologically relevant, as they introduce new ways of conceptualizing genomic functional profiling. Our very flexible approach allows for the inclusion of not just one kind of genomic measurement but many. It makes possible, for instance, to analyze expression measurement and genomic variation data at a time. This multidimensional gene set analysis approach is able to unravel genomic interactions that coordinately regulate functional blocks. We also indicate how to use data available in public repositories to asses gene importance within gene sets. Such importance can be included into our algorithms as a weight, improving performance of the analysis. But, more interestingly, it models functional blocks as non discrete entities, featuring a new concept of fuzzy gene set.