Analysis of metabolic networks of proteins hearkens back to the 1940s, but it was not until the late 1990s and early 2000s that computational data-driven genomic analyses to predict functional context and networks of genetic associations appeared in earnest. Since then, the interactomes of many model organisms are considered to have been well characterized, notably the Saccharomyces cerevisiae Interactome and the Drosophila interactome.
High throughput experimental approaches for discovering protein–protein interactions typically perform a version of the two-hybrid screening approach or tandem affinity purification followed by mass spectrometry. Information from experiments and literature curation are compiled into databases of protein interactions, such as DIP, and BioGRID. A more recent effort, HINT-KB, attempts to amalgamate most of the current PPI databases, but filtering systematically erroneous interactions as well as trying to correct for inherent sociological sampling biases in literature curated datasets.
Smaller human interactome networks have been described in the specific context of important drivers of many different disorders, including neurodegenerative disorders, autism and other psychiatric disorders, and cancer. Cancer gene networks have been particularly well studied, due in part to large genome initiatives such as The Cancer Genome Atlas (TCGA). A large portion of the mutational landscape including intra-tumoural heterogeneity has been mapped for most common types of cancers  (for example, breast cancer has been well studied), and many studies have also investigated the difference between active driver genes and passive passenger mutations in the context of cancer interaction networks.
The first attempts at large-scale integrative human interactome mapping occurred around 2005. Stetzl et al. used a protein matrix of 4500 baits and 5600 preys in a yeast two hybrid system to piece together the interactome, and Rual et al. performed a similar yeast-two hybrid study verified with co-affinity purification and correlation with other biological attributes, revealing more than 300 connections to 100 disease-associated proteins. Since those pioneering efforts, hundreds of similar studies have been conducted. Compiled databases such as UniHI provide platform for single entry. Futschik et al. performed a meta analysis of eight interactome maps and found that of 57 000 interacting proteins in total, there was a small (albeit statistically significant) overlap between the different databases, indicating considerable selection and detection biases.
In 2010, around 130 000 binary interactions in the interactome were described in the most popular databases, but many were verified with only one source. With the rapid development of high throughput methods, datasets still suffer from high rates of false positives and low coverage of the interactome. Tyagi et al. described a novel framework for incorporating structural complexes and binding interfaces for verification. This was part of much larger efforts for PPI verification; interaction networks are typically validated further by using a combination of coexpression profiles, protein structural information, Gene ontology terms, topological considerations, and colocalization before being considered “high-confidence”.
A recent resource paper (November 2014)  attempts to provide a more comprehensive proteome level map of the human interactome. It found vast uncharted territory in the human interactome, and used diverse methods to build a new interactome map correcting for curation bias, including probing all pairwise combinations of 13 000 protein products for interaction using Yeast two hybrid and co-affinity purification, in a massive coordinated effort across research labs in Canada and the United States. However, this still represents confirmation of but a fraction of expected interactions – around 30 000 of high confidence. Despite the coordinated efforts of many, the human interactome is still very much a work in progress.