Nowadays, large amounts of high-throughput data are available. Automatic with classical cell biology techniques which are employed in the analysis of cell functions, interactions, and how pathogens can exploit them in disease, are becoming available because of the huge advancements in both Genomics and Proteomics technologies. Analyzing and studying these vast amounts of data will enable researchers to uncover, clarify, and explain some aspects of gene products behavior and characteristics under a very diverse set of conditions. The biological data belong to different types. The integration of several types of data can help reduce the effect of problems each data source has. The focus or our work and among the very important tasks in the bioinformatics field are functional module discovery and discriminative pattern. In functional module discovery, the goal is to find groups of genes that interact to perform different processes in the living organism. Discriminative patterns mining aims at discovering groups of proteins that can be classified as related to a specific phenotype. Understanding what genes, or proteins, are involved in biological phenomena can lead to advancements in related medical and pharmaceutical research. Many research has be done in this area. The two main sources of data used in my work are the gene expression and the protein-protein interaction network. The expression data shows how genes react in several conditions. The interaction network represents real protein cooperations occurring in the living cell. Our research efforts proved to show competitive performance with well established methods as illustrated in this document.