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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.
Highlights
The richness of information available in the vast biomedical literature has motivated many studies and resources to include textual data as an information source [1,2,3,4]
The automated processing of all available PM abstracts and PubMed Central (PMC) full-texts yielded more than 40 million detailed biomolecular events among 76 million gene/protein mentions (Table 3)
We have presented a text mining analysis that combines structured event extraction with gene normalization – two major lines of research in the BioNLP community – to process all PubMed abstracts and all open access full texts in PubMed Central
Summary
The richness of information available in the vast biomedical literature has motivated many studies and resources to include textual data as an information source [1,2,3,4]. The development of fully automated text mining techniques has attracted wide interest, resulting in several general-purpose stand-alone text mining tools [5,6,7,8,9,10,11]. In this context, text mining involves two key challenges: the automated extraction of formal representations of statements from text, and the identification of the real-world objects, such as genes and proteins, that these statements refer to (Figure 1). The scope of event extraction has been further broadened for epigenetics, post-translational modifications, and core protein information such as protein domains and complexes [16,17]
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