Which are the available biomedical text mining tools for the detection of protein-protein interactions?
Protein-protein interactions (PPI) can be extracted from biomedical literature using text mining approaches. These approaches have been classified into two categories, the statistical calculation of the co-occurrence of proteins and the computational linguistic method. Moreover, bioinformatics methods based on sequence, structural, or evolutionary information have been developed to predict protein-protein interactions. The available state-of-the-art biomedical text mining tools are: eFIP (Extracting Functional Impact of Phosphorylation), a system for text mining of protein interaction networks of phosphorylated proteins; GeneView, a suite of state-of-the-art text-mining tools designed for the automated identification of protein–protein interactions; PPI finder, a text mining tool for human protein-protein interactions based on computational linguistic methods. PPI Finder system consists of the Information Retrieval module and Information Extraction module; PreBIND and Textomy are two components of a biomedical literature-mining system optimized to discover protein-protein interactions using a support vector machine; BioMap system is optimized for the identification of protein-protein interactions from large biomedical literature datasets; Protopia searches for and integrates protein-protein interactions and the information about them contained in five different Protein Interaction Web Databases; STRING uses Natural Language Processing to extract a subset of semantically specified known and predicted protein-protein interactions.