Technological advancements in the field of genetics have led not only to an abundance of experimental data, but also caused an exponential increase of the number of published biomolecular studies. Text mining is widely accepted as a promising technique to help researchers in the life sciences deal with the amount of available literature. This paper presents a freely available web application built on top of 21.3 million detailed biomolecular events extracted from all PubMed abstracts. These text mining results were generated by a state-of-the-art event extraction system and enriched with gene family associations and abstract generalizations, accounting for lexical variants and synonymy. The EVEX resource locates relevant literature on phosphorylation, regulation targets, binding partners, and several other biomolecular events and assigns confidence values to these events. The search function accepts official gene/protein symbols as well as common names from all species. Finally, the web application is a powerful tool for generating homology-based hypotheses as well as novel, indirect associations between genes and proteins such as coregulators. 1. Introduction The field of natural language processing for biomolecular texts (BioNLP) aims at large-scale text mining in support of life science research. Its primary motivation is the enormous amount of available scientific literature, which makes it essentially impossible to rapidly gain an overview of prior research results other than in a very narrow domain of interest. Among the typical use cases for BioNLP applications are support for database curation, linking experimental data with relevant literature, content visualization, and hypothesis generation—all of these tasks require processing and summarizing large amounts of individual research articles. Among the most heavily studied tasks in BioNLP is the extraction of information about known associations between biomolecular entities, primarily genes, and gene products, and this task has recently seen much progress in two general directions. First, relationships between biomolecular entities are now being extracted in much greater detail. Until recently, the focus was on extracting untyped and undirected binary relations which, while stating that there is some relationship between two objects, gave little additional information about the nature of the relationship. Recognizing that extracting such relations may not provide sufficient detail for wider adoption of text mining in the biomedical community, the focus is currently shifting towards a more
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