Date Published: June 6, 2012
Publisher: Hindawi Publishing Corporation
Author(s): Sofie Van Landeghem, Kai Hakala, Samuel Rönnqvist, Tapio Salakoski, Yves Van de Peer, Filip Ginter.
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.
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.
This section describes the original event data, as well as a ranking procedure that sorts events according to their reliability. Further, two abstract layers are defined on top of the complex event structures, enabling coarse grouping of similar events, and providing an intuitive pairwise point of view that allows fast retrieval of interesting gene/protein pairs. Finally, we describe a hypothesis generation module that finds missing links between two entities, allowing the user to retrieve proteins with common binding partners or genes that act as coregulators of a group of common target genes.
In this section, we present the evaluation of the EVEX resource from several points of view. First, we discuss the performance of the event extraction system used to produce the core set of events in EVEX, reviewing a number of published evaluations both within the BioNLP Shared Task and in other domains. Second, we present several evaluations of the methods and data employed specifically in the EVEX resource in addition to the core event predictions: we review existing results as well as present new evaluations of the confidence scores and their correlation with event precision, the family-based generalization algorithms, and the novel event refinement algorithms introduced above. Finally, we discuss two biologically motivated applications of EVEX, demonstrating the usability of EVEX in real-world use cases.
To illustrate the functionality and features of the web application, we present a use case on a specific budding yeast gene, Mec1, which is conserved in S. pombe, S. cerevisiae, K. lactis, E. gossypii, M. grisea, and N. crassa. Mec1 is required for meiosis and plays a critical role in the maintenance of genome stability. Furthermore, it is considered to be a homolog of the mammalian ATR/ATM, a signal transduction protein .
This paper presents a publicly available web application providing access to over 21 million detailed events among more than 40 million identified gene/protein symbols in nearly 6 million PubMed titles and abstracts. This dataset is the result of processing the entire collection of PubMed titles and abstracts through a state-of-the-art event extraction system and is regularly updated as new citations are added to PubMed. The extracted events provide a detailed representation of the textual statements, allowing for recursively nested events and different event types ranging from phosphorylation to catabolism and regulation. The EVEX web application is the first publicly released resource that provides intuitive access to these detailed event-based text mining results.