NLM-Chem-BC7: manually annotated full-text resources for chemical entity annotation and indexing in biomedical articles.

The automatic recognition of chemical names and their corresponding database identifiers in biomedical text is an important first step for many downstream text-mining applications. The task is even more challenging when considering the identification of these entities in the article's full text and, furthermore, the identification of candidate substances for that article's metadata [Medical Subject Heading (MeSH) article indexing]. The National Library of Medicine (NLM)-Chem track at BioCreative VII aimed to foster the development of algorithms that can predict with high quality the chemical entities in the biomedical literature and further identify the chemical substances that are candidates for article indexing. As a result of this challenge, the NLM-Chem track produced two comprehensive, manually curated corpora annotated with chemical entities and indexed with chemical substances: the chemical identification corpus and the chemical indexing corpus. The NLM-Chem BioCreative VII (NLM-Chem-BC7) Chemical Identification corpus consists of 204 full-text PubMed Central (PMC) articles, fully annotated for chemical entities by 12 NLM indexers for both span (i.e. named entity recognition) and normalization (i.e. entity linking) using MeSH. This resource was used for the training and testing of the Chemical Identification task to evaluate the accuracy of algorithms in predicting chemicals mentioned in recently published full-text articles. The NLM-Chem-BC7 Chemical Indexing corpus consists of 1333 recently published PMC articles, equipped with chemical substance indexing by manual experts at the NLM. This resource was used for the evaluation of the Chemical Indexing task, which evaluated the accuracy of algorithms in predicting the chemicals that should be indexed, i.e. appear in the listing of MeSH terms for the document. This set was further enriched after the challenge in two ways: (i) 11 NLM indexers manually verified each of the candidate terms appearing in the prediction results of the challenge participants, but not in the MeSH indexing, and the chemical indexing terms appearing in the MeSH indexing list, but not in the prediction results, and (ii) the challenge organizers algorithmically merged the chemical entity annotations in the full text for all predicted chemical entities and used a statistical approach to keep those with the highest degree of confidence. As a result, the NLM-Chem-BC7 Chemical Indexing corpus is a gold-standard corpus for chemical indexing of journal articles and a silver-standard corpus for chemical entity identification in full-text journal articles. Together, these resources are currently the most comprehensive resources for chemical entity recognition, and we demonstrate improvements in the chemical entity recognition algorithms. We detail the characteristics of these novel resources and make them available for the community. Database URL: https://ftp.ncbi.nlm.nih.gov/pub/lu/NLM-Chem-BC7-corpus/.

NLM-Chem, a new resource for chemical entity recognition in PubMed full text literature.

Automatically identifying chemical and drug names in scientific publications advances information access for this important class of entities in a variety of biomedical disciplines by enabling improved retrieval and linkage to related concepts. While current methods for tagging chemical entities were developed for the article title and abstract, their performance in the full article text is substantially lower. However, the full text frequently contains more detailed chemical information, such as the properties of chemical compounds, their biological effects and interactions with diseases, genes and other chemicals. We therefore present the NLM-Chem corpus, a full-text resource to support the development and evaluation of automated chemical entity taggers. The NLM-Chem corpus consists of 150 full-text articles, doubly annotated by ten expert NLM indexers, with ~5000 unique chemical name annotations, mapped to ~2000 MeSH identifiers. We also describe a substantially improved chemical entity tagger, with automated annotations for all of PubMed and PMC freely accessible through the PubTator web-based interface and API. The NLM-Chem corpus is freely available.

Identification of a sporozoite-specific antigen from Toxoplasma gondii.

Reduction of risk for human and food animal infection with Toxoplasma gondii is hampered by the lack of epidemiological data documenting the predominant routes of infection (oocyst vs. tissue cyst consumption) in horizontally transmitted toxoplasmosis. Existing serological assays can determine previous exposure to the parasite, but not the route of infection. We have used difference gel electrophoresis, in combination with tandem mass spectroscopy and Western blot, to identify a sporozoite-specific protein (T. gondii embryogenesis-related protein [TgERP]), which elicited antibody and differentiated oocyst- versus tissue cyst-induced infection in pigs and mice. The recombinant protein was selected from a cDNA library constructed from T. gondii sporozoites; this protein was used in Western blots and probed with sera from T. gondii -infected humans. Serum antibody to TgERP was detected in humans within 6-8 mo of initial oocyst-acquired infection. Of 163 individuals in the acute stage of infection (anti- T. gondii IgM detected in sera, or < 30 in the IgG avidity test), 103 (63.2%) had detectable antibodies that reacted with TgERP. Of 176 individuals with unknown infection route and in the chronic stage of infection (no anti- T. gondii IgM detected in sera, or > 30 in the IgG avidity test), antibody to TgERP was detected in 31 (17.6%). None of the 132 uninfected individuals tested had detectable antibody to TgERP. These data suggest that TgERP may be useful in detecting exposure to sporozoites in early T. gondii infection and implicates oocysts as the agent of infection.