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Muenchen Wissenschaftszentrum Weihenstephan, 85350 Freising, Germany. PMID: 24214996 PMCID: PMC3965096 DOI: 10.1093/nar/gkt1079. Abstract. Knowledge about non-interacting proteins (NIPs) is important for training the algorithms g downloadming to predict protein-protein interactions (PPIs) and for assessing the false positive rates of PPI detection efforts. We present the second version of Negatome, a database of proteins and protein domains that are unlikely to engage in physical interactions (available online at http://mips.helmholtz-muenchen.de/proj/ppi/negatome). Negatome is derived by manual curation of literature and by analyzing three-dimensional structures of protein complexes. The main methodological innovation in Negatome 2.0 is the utilization of an advanced text mining procedure to guide the manual annotation process. Potential non-interactions were identified by a modified version of Excerbt, a text mining tool based on semantic sentence analysis. Manual verification shows that nearly a half of the text mining results with the highest confidence values correspond to NIP pairs. Compared to the first version the contents of the database have grown by over 300%. Figures. Manual assessment of the text. Manual assessment of the text mining performance. The figure shows the number of. Flowchart explaining how Negatome 2. Flowchart explaining how Negatome 2.0 data are generated, merged with Negatome