The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text

By E. Pafilis, S. Frankild, L. Fanini, S. Faulwetter, Christina Pavloudi, A. Vasileiadou, Christos Arvanitidis, Lars Juhl Jensen. 2013

221 citations recorded by the source corpus.

The exponential growth of the biomedical literature is making the need for efficient, accurate text-mining tools increasingly clear. The identification of named biological entities in text is a central and difficult task. We have developed an efficient algorithm and implementation of a dictionary-based approach to named entity recognition, which we here use to identify names of species and other taxa in text. The tool, SPECIES, is more than an order of magnitude faster and as accurate as existing tools. The precision and recall was assessed both on an existing gold-standard corpus and on a new corpus of 800 abstracts, which were manually annotated after the development of the tool. The corpus comprises abstracts from journals selected to represent many taxonomic groups, which gives insights into which types of organism names are hard to detect and which are easy. Finally, we have tagged organism names in the entire Medline database and developed a web resource, ORGANISMS, that makes the results accessible to the broad community of biologists. The SPECIES software is open source and can be downloaded from http://species.jensenlab.org along with dictionary files and the manually annotated gold-standard corpus. The ORGANISMS web resource can be found at http://organisms.jensenlab.org.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

The SIDER database of drugs and side effects (2015, methodology): “After completing the dictionaries, entities were recognized using an NER engine that also accounts for orthographic variation, specifically case variation and insertion/removal of hyphens and spaces (16).”
Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data (2014, methodology): “The Resources Tab has been augmented with links to disease––gene resources (FunDO (18), Bioconductor GeneAnswers (19)) and downloads of the DO Neo4j database on GitHub.”
COMPARTMENTS: unification and visualization of protein subcellular localization evidence (2014, methodology): “We matched these dictionaries against all Medline abstracts using an efficient named entity recognition engine described elsewhere (51).”
Transfer learning for biomedical named entity recognition with neural networks (2018, unknown): “, 2014), "S800" for species (Pafilis et al., 2013) and "DECA" for genes/proteins (Wang et al.”
DISEASES: Text mining and data integration of disease–gene associations (2014, methodology): “The algorithm is described in full detail elsewhere [38], but is summarized here for completeness.”
miRBase: from microRNA sequences to function (2018, background): “Recognizing gene names, or even species names, in full-text articles is surprisingly challenging (28–30).”