The interpretation of phosphoproteomics data sets is crucial for generating hypotheses that guide therapeutic solutions, yet not many techniques have been applied to this type of analysis. This paper intends to give an overview about the two main standard techniques that can be applied to the analysis of these large scale data sets. These are data-driven or exploratory techniques based on a statistical model and topology-driven methods that analyze the signaling network from a dynamical standpoint. While employing different paradigms, these algorithms will detect unique "fingerprints" by revealing the intricate interactions at the proteome level and will support the experimental environment for novel therapeutics for many diseases.