Large-scale event extraction from literature with multi-level gene normalization

Abstract

Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons - Attribution - Share Alike (CC BY-SA) license.

Figure 1. Illustration of event extraction and gene normalization.

The gene mentions recognised in text are in red and the extracted event structures in blue. The normalization algorithm further maps the ambiguous gene mentions to unique database identifiers (in green).

Figure 2. Overview of the various steps and programs involved in this study.

The black arrows represent previously published tools, which have all been integrated in this study to create a unified text mining pipeline. Furthermore, the various opportunities for combining the different methods for gene normalization are presented by the colored edges and discussed in detail in the text.

Figure 3. The most frequently occurring organisms, by the number of associated events found in literature.

This plot illustrates that this study covers normalized event data across all domains and kingdoms. It was created with iTOL , and the phylogenetic tree is constructed through the information available at NCBI Taxonomy .

Figure 4. Event extraction performance.

Both the evaluations of the BioNLP ST'11 GE task development set (3021 events, ST evaluation scripts) as well as a fully random sample (200 events, manually evaluated) are depicted. Events are ordered by their confidence scores, and plotted at different precision/recall trade-off points.

Figure 5. Events mapped to biomolecular pathways from KEGG.

A) Close interactions of p53 from KEGG pathway hsa04115. B) The highest confidence predicted event from EVEX for each directed KEGG association. All are correct and correspond to the KEGG interaction type. Event visualizations were made with stav . C) The number of events in the text mining dataset for each undirected protein pair. Pairs not corresponding to a direct molecular interaction in A) are shown in red.