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Proteins have been proven to be among the most significant cellular molecules as they participate in most cellular functionalities. Researchers have deployed a variety of experimental methods for the identification of Protein–Protein Interactions (PPIs). The emergence of high-throughput experimental techniques for the prediction of PPIs, revealed a wide range of PPIs in many organisms. This information alongside with information from small scale experimental techniques has been stored in public available databases and repositories. It is well-known that experimental data include many false positive predictions and provide only low coverage on the full interactomes. This fact has led to the design and development of many computational methods for the prediction of PPIs (Theofilatos et al., 2011).  The experimental PPI data have been extensively used in many studies during the last decades and their availability gave a significant boost in training new algorithmic models for the prediction of PPIs and in the overall analysis of PPI data.  Despite the promising results of algorithmic solutions for PPIs' analysis which fostered molecular biology research, in our opinion the research on computational methods for analyzing PPI data has been recently stagnated. Using the online tool MLtrends introduced by Palidwor and Andrade-Navarro (2010) in a preliminary investigation we have observed that the publications related to the search term “protein–protein interactions” AND analysis (abstract and title were searched for this term) present a constant increase in absolute numbers. However, when applying normalization by dividing with the total number of annual publications, we observe a relatively stable percentage of publications related PPI analysis in the last decade. In contrast, systems biology publications present a big positive slope in the last decade even when normalized by the total number of annual publications. This diversification shows that even if the actual total number of publications related to PPIs analysis is increasing as the total number of scientific journals is increasing in the last decade, their total impact on the systems biology domain is decreasing. Additionally, only a few PPI based research works have been published lately with significant impact in clinical research and translational bioinformatics.  In this paper, first we summarize the developments on computational analysis of PPI data and second, we present our belief about the future of PPI data analysis emphasizing in presenting the constraints that have delayed the transition from the current methodologies to a holistic bioinformatics approach, for linking biological and clinical data. Specific solutions are also proposed for all these constraints in order to achieve the optimal exploitation of PPI bioinformatics' approaches.

Quo vadis1 computational analysis of PPI data or why the future isn't here yet