PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge.

PubTator 3.0 (https://www.ncbi.nlm.nih.gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for key concepts like proteins, genetic variants, diseases and chemicals. It currently provides over one billion entity and relation annotations across approximately 36 million PubMed abstracts and 6 million full-text articles from the PMC open access subset, updated weekly. PubTator 3.0's online interface and API utilize these precomputed entity relations and synonyms to provide advanced search capabilities and enable large-scale analyses, streamlining many complex information needs. We showcase the retrieval quality of PubTator 3.0 using a series of entity pair queries, demonstrating that PubTator 3.0 retrieves a greater number of articles than either PubMed or Google Scholar, with higher precision in the top 20 results. We further show that integrating ChatGPT (GPT-4) with PubTator APIs dramatically improves the factuality and verifiability of its responses. In summary, PubTator 3.0 offers a comprehensive set of features and tools that allow researchers to navigate the ever-expanding wealth of biomedical literature, expediting research and unlocking valuable insights for scientific discovery.

LitCovid in 2022: an information resource for the COVID-19 literature.

LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/)-first launched in February 2020-is a first-of-its-kind literature hub for tracking up-to-date published research on COVID-19. The number of articles in LitCovid has increased from 55 000 to ∼300 000 over the past 2.5 years, with a consistent growth rate of ∼10 000 articles per month. In addition to the rapid literature growth, the COVID-19 pandemic has evolved dramatically. For instance, the Omicron variant has now accounted for over 98% of new infections in the United States. In response to the continuing evolution of the COVID-19 pandemic, this article describes significant updates to LitCovid over the last 2 years. First, we introduced the long Covid collection consisting of the articles on COVID-19 survivors experiencing ongoing multisystemic symptoms, including respiratory issues, cardiovascular disease, cognitive impairment, and profound fatigue. Second, we provided new annotations on the latest COVID-19 strains and vaccines mentioned in the literature. Third, we improved several existing features with more accurate machine learning algorithms for annotating topics and classifying articles relevant to COVID-19. LitCovid has been widely used with millions of accesses by users worldwide on various information needs and continues to play a critical role in collecting, curating and standardizing the latest knowledge on the COVID-19 literature.

AIONER: all-in-one scheme-based biomedical named entity recognition using deep learning.

MOTIVATION: Biomedical named entity recognition (BioNER) seeks to automatically recognize biomedical entities in natural language text, serving as a necessary foundation for downstream text mining tasks and applications such as information extraction and question answering. Manually labeling training data for the BioNER task is costly, however, due to the significant domain expertise required for accurate annotation. The resulting data scarcity causes current BioNER approaches to be prone to overfitting, to suffer from limited generalizability, and to address a single entity type at a time (e.g. gene or disease). RESULTS: We therefore propose a novel all-in-one (AIO) scheme that uses external data from existing annotated resources to enhance the accuracy and stability of BioNER models. We further present AIONER, a general-purpose BioNER tool based on cutting-edge deep learning and our AIO schema. We evaluate AIONER on 14 BioNER benchmark tasks and show that AIONER is effective, robust, and compares favorably to other state-of-the-art approaches such as multi-task learning. We further demonstrate the practical utility of AIONER in three independent tasks to recognize entity types not previously seen in training data, as well as the advantages of AIONER over existing methods for processing biomedical text at a large scale (e.g. the entire PubMed data). AVAILABILITY AND IMPLEMENTATION: The source code, trained models and data for AIONER are freely available at https://github.com/ncbi/AIONER.

Ten tips for a text-mining-ready article: How to improve automated discoverability and interpretability.

Data-driven research in biomedical science requires structured, computable data. Increasingly, these data are created with support from automated text mining. Text-mining tools have rapidly matured: although not perfect, they now frequently provide outstanding results. We describe 10 straightforward writing tips-and a web tool, PubReCheck-guiding authors to help address the most common cases that remain difficult for text-mining tools. We anticipate these guides will help authors' work be found more readily and used more widely, ultimately increasing the impact of their work and the overall benefit to both authors and readers. PubReCheck is available at http://www.ncbi.nlm.nih.gov/research/pubrecheck.

PubTator central: automated concept annotation for biomedical full text articles.

PubTator Central (https://www.ncbi.nlm.nih.gov/research/pubtator/) is a web service for viewing and retrieving bioconcept annotations in full text biomedical articles. PubTator Central (PTC) provides automated annotations from state-of-the-art text mining systems for genes/proteins, genetic variants, diseases, chemicals, species and cell lines, all available for immediate download. PTC annotates PubMed (29 million abstracts) and the PMC Text Mining subset (3 million full text articles). The new PTC web interface allows users to build full text document collections and visualize concept annotations in each document. Annotations are downloadable in multiple formats (XML, JSON and tab delimited) via the online interface, a RESTful web service and bulk FTP. Improved concept identification systems and a new disambiguation module based on deep learning increase annotation accuracy, and the new server-side architecture is significantly faster. PTC is synchronized with PubMed and PubMed Central, with new articles added daily. The original PubTator service has served annotated abstracts for ∼300 million requests, enabling third-party research in use cases such as biocuration support, gene prioritization, genetic disease analysis, and literature-based knowledge discovery. We demonstrate the full text results in PTC significantly increase biomedical concept coverage and anticipate this expansion will both enhance existing downstream applications and enable new use cases.

ezTag: tagging biomedical concepts via interactive learning.

Recently, advanced text-mining techniques have been shown to speed up manual data curation by providing human annotators with automated pre-annotations generated by rules or machine learning models. Due to the limited training data available, however, current annotation systems primarily focus only on common concept types such as genes or diseases. To support annotating a wide variety of biological concepts with or without pre-existing training data, we developed ezTag, a web-based annotation tool that allows curators to perform annotation and provide training data with humans in the loop. ezTag supports both abstracts in PubMed and full-text articles in PubMed Central. It also provides lexicon-based concept tagging as well as the state-of-the-art pre-trained taggers such as TaggerOne, GNormPlus and tmVar. ezTag is freely available at http://eztag.bioqrator.org.

Crowdsourcing in biomedicine: challenges and opportunities.

The use of crowdsourcing to solve important but complex problems in biomedical and clinical sciences is growing and encompasses a wide variety of approaches. The crowd is diverse and includes online marketplace workers, health information seekers, science enthusiasts and domain experts. In this article, we review and highlight recent studies that use crowdsourcing to advance biomedicine. We classify these studies into two broad categories: (i) mining big data generated from a crowd (e.g. search logs) and (ii) active crowdsourcing via specific technical platforms, e.g. labor markets, wikis, scientific games and community challenges. Through describing each study in detail, we demonstrate the applicability of different methods in a variety of domains in biomedical research, including genomics, biocuration and clinical research. Furthermore, we discuss and highlight the strengths and limitations of different crowdsourcing platforms. Finally, we identify important emerging trends, opportunities and remaining challenges for future crowdsourcing research in biomedicine.

TaggerOne: joint named entity recognition and normalization with semi-Markov Models.

MOTIVATION: Text mining is increasingly used to manage the accelerating pace of the biomedical literature. Many text mining applications depend on accurate named entity recognition (NER) and normalization (grounding). While high performing machine learning methods trainable for many entity types exist for NER, normalization methods are usually specialized to a single entity type. NER and normalization systems are also typically used in a serial pipeline, causing cascading errors and limiting the ability of the NER system to directly exploit the lexical information provided by the normalization. METHODS: We propose the first machine learning model for joint NER and normalization during both training and prediction. The model is trainable for arbitrary entity types and consists of a semi-Markov structured linear classifier, with a rich feature approach for NER and supervised semantic indexing for normalization. We also introduce TaggerOne, a Java implementation of our model as a general toolkit for joint NER and normalization. TaggerOne is not specific to any entity type, requiring only annotated training data and a corresponding lexicon, and has been optimized for high throughput. RESULTS: We validated TaggerOne with multiple gold-standard corpora containing both mention- and concept-level annotations. Benchmarking results show that TaggerOne achieves high performance on diseases (NCBI Disease corpus, NER f-score: 0.829, normalization f-score: 0.807) and chemicals (BioCreative 5 CDR corpus, NER f-score: 0.914, normalization f-score 0.895). These results compare favorably to the previous state of the art, notwithstanding the greater flexibility of the model. We conclude that jointly modeling NER and normalization greatly improves performance. AVAILABILITY AND IMPLEMENTATION: The TaggerOne source code and an online demonstration are available at: http://www.ncbi.nlm.nih.gov/bionlp/taggerone CONTACT: zhiyong.lu@nih.gov SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Beyond accuracy: creating interoperable and scalable text-mining web services.

UNLABELLED: The biomedical literature is a knowledge-rich resource and an important foundation for future research. With over 24 million articles in PubMed and an increasing growth rate, research in automated text processing is becoming increasingly important. We report here our recently developed web-based text mining services for biomedical concept recognition and normalization. Unlike most text-mining software tools, our web services integrate several state-of-the-art entity tagging systems (DNorm, GNormPlus, SR4GN, tmChem and tmVar) and offer a batch-processing mode able to process arbitrary text input (e.g. scholarly publications, patents and medical records) in multiple formats (e.g. BioC). We support multiple standards to make our service interoperable and allow simpler integration with other text-processing pipelines. To maximize scalability, we have preprocessed all PubMed articles, and use a computer cluster for processing large requests of arbitrary text. AVAILABILITY AND IMPLEMENTATION: Our text-mining web service is freely available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/#curl CONTACT: : Zhiyong.Lu@nih.gov.

Challenges in clinical natural language processing for automated disorder normalization.

BACKGROUND: Identifying key variables such as disorders within the clinical narratives in electronic health records has wide-ranging applications within clinical practice and biomedical research. Previous research has demonstrated reduced performance of disorder named entity recognition (NER) and normalization (or grounding) in clinical narratives than in biomedical publications. In this work, we aim to identify the cause for this performance difference and introduce general solutions. METHODS: We use closure properties to compare the richness of the vocabulary in clinical narrative text to biomedical publications. We approach both disorder NER and normalization using machine learning methodologies. Our NER methodology is based on linear-chain conditional random fields with a rich feature approach, and we introduce several improvements to enhance the lexical knowledge of the NER system. Our normalization method - never previously applied to clinical data - uses pairwise learning to rank to automatically learn term variation directly from the training data. RESULTS: We find that while the size of the overall vocabulary is similar between clinical narrative and biomedical publications, clinical narrative uses a richer terminology to describe disorders than publications. We apply our system, DNorm-C, to locate disorder mentions and in the clinical narratives from the recent ShARe/CLEF eHealth Task. For NER (strict span-only), our system achieves precision=0.797, recall=0.713, f-score=0.753. For the normalization task (strict span+concept) it achieves precision=0.712, recall=0.637, f-score=0.672. The improvements described in this article increase the NER f-score by 0.039 and the normalization f-score by 0.036. We also describe a high recall version of the NER, which increases the normalization recall to as high as 0.744, albeit with reduced precision. DISCUSSION: We perform an error analysis, demonstrating that NER errors outnumber normalization errors by more than 4-to-1. Abbreviations and acronyms are found to be frequent causes of error, in addition to the mentions the annotators were not able to identify within the scope of the controlled vocabulary. CONCLUSION: Disorder mentions in text from clinical narratives use a rich vocabulary that results in high term variation, which we believe to be one of the primary causes of reduced performance in clinical narrative. We show that pairwise learning to rank offers high performance in this context, and introduce several lexical enhancements - generalizable to other clinical NER tasks - that improve the ability of the NER system to handle this variation. DNorm-C is a high performing, open source system for disorders in clinical text, and a promising step toward NER and normalization methods that are trainable to a wide variety of domains and entities. (DNorm-C is open source software, and is available with a trained model at the DNorm demonstration website: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/#DNorm.).

DNorm: disease name normalization with pairwise learning to rank.

MOTIVATION: Despite the central role of diseases in biomedical research, there have been much fewer attempts to automatically determine which diseases are mentioned in a text-the task of disease name normalization (DNorm)-compared with other normalization tasks in biomedical text mining research. METHODS: In this article we introduce the first machine learning approach for DNorm, using the NCBI disease corpus and the MEDIC vocabulary, which combines MeSH® and OMIM. Our method is a high-performing and mathematically principled framework for learning similarities between mentions and concept names directly from training data. The technique is based on pairwise learning to rank, which has not previously been applied to the normalization task but has proven successful in large optimization problems for information retrieval. RESULTS: We compare our method with several techniques based on lexical normalization and matching, MetaMap and Lucene. Our algorithm achieves 0.782 micro-averaged F-measure and 0.809 macro-averaged F-measure, an increase over the highest performing baseline method of 0.121 and 0.098, respectively. AVAILABILITY: The source code for DNorm is available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/DNorm, along with a web-based demonstration and links to the NCBI disease corpus. Results on PubMed abstracts are available in PubTator: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator .

NCBI disease corpus: a resource for disease name recognition and concept normalization.

Information encoded in natural language in biomedical literature publications is only useful if efficient and reliable ways of accessing and analyzing that information are available. Natural language processing and text mining tools are therefore essential for extracting valuable information, however, the development of powerful, highly effective tools to automatically detect central biomedical concepts such as diseases is conditional on the availability of annotated corpora. This paper presents the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. Each PubMed abstract was manually annotated by two annotators with disease mentions and their corresponding concepts in Medical Subject Headings (MeSH®) or Online Mendelian Inheritance in Man (OMIM®). Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations. Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two annotation phases. In this setting, a high inter-annotator agreement was observed. Finally, all results were checked against annotations of the rest of the corpus to assure corpus-wide consistency. The public release of the NCBI disease corpus contains 6892 disease mentions, which are mapped to 790 unique disease concepts. Of these, 88% link to a MeSH identifier, while the rest contain an OMIM identifier. We were able to link 91% of the mentions to a single disease concept, while the rest are described as a combination of concepts. In order to help researchers use the corpus to design and test disease identification methods, we have prepared the corpus as training, testing and development sets. To demonstrate its utility, we conducted a benchmarking experiment where we compared three different knowledge-based disease normalization methods with a best performance in F-measure of 63.7%. These results show that the NCBI disease corpus has the potential to significantly improve the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks. The NCBI disease corpus, guidelines and other associated resources are available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/.

PubMed and beyond: biomedical literature search in the age of artificial intelligence.

Biomedical research yields vast information, much of which is only accessible through the literature. Consequently, literature search is crucial for healthcare and biomedicine. Recent improvements in artificial intelligence (AI) have expanded functionality beyond keywords, but they might be unfamiliar to clinicians and researchers. In response, we present an overview of over 30 literature search tools tailored to common biomedical use cases, aiming at helping readers efficiently fulfill their information needs. We first discuss recent improvements and continued challenges of the widely used PubMed. Then, we describe AI-based literature search tools catering to five specific information needs: 1. Evidence-based medicine. 2. Precision medicine and genomics. 3. Searching by meaning, including questions. 4. Finding related articles with literature recommendation. 5. Discovering hidden associations through literature mining. Finally, we discuss the impacts of recent developments of large language models such as ChatGPT on biomedical information seeking.

PubTator 3.0: an AI-powered Literature Resource for Unlocking Biomedical Knowledge.

PubTator 3.0 (https://www.ncbi.nlm.nih.gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for key concepts like proteins, genetic variants, diseases, and chemicals. It currently provides over one billion entity and relation annotations across approximately 36 million PubMed abstracts and 6 million full-text articles from the PMC open access subset, updated weekly. PubTator 3.0's online interface and API utilize these precomputed entity relations and synonyms to provide advanced search capabilities and enable large-scale analyses, streamlining many complex information needs. We showcase the retrieval quality of PubTator 3.0 using a series of entity pair queries, demonstrating that PubTator 3.0 retrieves a greater number of articles than either PubMed or Google Scholar, with higher precision in the top 20 results. We further show that integrating ChatGPT (GPT-4) with PubTator APIs dramatically improves the factuality and verifiability of its responses. In summary, PubTator 3.0 offers a comprehensive set of features and tools that allow researchers to navigate the ever-expanding wealth of biomedical literature, expediting research and unlocking valuable insights for scientific discovery.

EnzChemRED, a rich enzyme chemistry relation extraction dataset.

Expert curation is essential to capture knowledge of enzyme functions from the scientific literature in FAIR open knowledgebases but cannot keep pace with the rate of new discoveries and new publications. In this work we present EnzChemRED, for Enzyme Chemistry Relation Extraction Dataset, a new training and benchmarking dataset to support the development of Natural Language Processing (NLP) methods such as (large) language models that can assist enzyme curation. EnzChemRED consists of 1,210 expert curated PubMed abstracts in which enzymes and the chemical reactions they catalyze are annotated using identifiers from the UniProt Knowledgebase (UniProtKB) and the ontology of Chemical Entities of Biological Interest (ChEBI). We show that fine-tuning pre-trained language models with EnzChemRED can significantly boost their ability to identify mentions of proteins and chemicals in text (Named Entity Recognition, or NER) and to extract the chemical conversions in which they participate (Relation Extraction, or RE), with average F1 score of 86.30% for NER, 86.66% for RE for chemical conversion pairs, and 83.79% for RE for chemical conversion pairs and linked enzymes. We combine the best performing methods after fine-tuning using EnzChemRED to create an end-to-end pipeline for knowledge extraction from text and apply this to abstracts at PubMed scale to create a draft map of enzyme functions in literature to guide curation efforts in UniProtKB and the reaction knowledgebase Rhea. The EnzChemRED corpus is freely available at https://ftp.expasy.org/databases/rhea/nlp/.

Comprehensively identifying Long Covid articles with human-in-the-loop machine learning.

A significant percentage of COVID-19 survivors experience ongoing multisystemic symptoms that often affect daily living, a condition known as Long Covid or post-acute-sequelae of SARS-CoV-2 infection. However, identifying scientific articles relevant to Long Covid is challenging since there is no standardized or consensus terminology. We developed an iterative human-in-the-loop machine learning framework combining data programming with active learning into a robust ensemble model, demonstrating higher specificity and considerably higher sensitivity than other methods. Analysis of the Long Covid Collection shows that (1) most Long Covid articles do not refer to Long Covid by any name, (2) when the condition is named, the name used most frequently in the literature is Long Covid, and (3) Long Covid is associated with disorders in a wide variety of body systems. The Long Covid Collection is updated weekly and is searchable online at the LitCovid portal: https://www.ncbi.nlm.nih.gov/research/coronavirus/docsum?filters=e_condition.LongCovid.

Chemical identification and indexing in full-text articles: an overview of the NLM-Chem track at BioCreative VII.

The BioCreative National Library of Medicine (NLM)-Chem track calls for a community effort to fine-tune automated recognition of chemical names in the biomedical literature. Chemicals are one of the most searched biomedical entities in PubMed, and-as highlighted during the coronavirus disease 2019 pandemic-their identification may significantly advance research in multiple biomedical subfields. While previous community challenges focused on identifying chemical names mentioned in titles and abstracts, the full text contains valuable additional detail. We, therefore, organized the BioCreative NLM-Chem track as a community effort to address automated chemical entity recognition in full-text articles. The track consisted of two tasks: (i) chemical identification and (ii) chemical indexing. The chemical identification task required predicting all chemicals mentioned in recently published full-text articles, both span [i.e. named entity recognition (NER)] and normalization (i.e. entity linking), using Medical Subject Headings (MeSH). The chemical indexing task required identifying which chemicals reflect topics for each article and should therefore appear in the listing of MeSH terms for the document in the MEDLINE article indexing. This manuscript summarizes the BioCreative NLM-Chem track and post-challenge experiments. We received a total of 85 submissions from 17 teams worldwide. The highest performance achieved for the chemical identification task was 0.8672 F-score (0.8759 precision and 0.8587 recall) for strict NER performance and 0.8136 F-score (0.8621 precision and 0.7702 recall) for strict normalization performance. The highest performance achieved for the chemical indexing task was 0.6073 F-score (0.7417 precision and 0.5141 recall). This community challenge demonstrated that (i) the current substantial achievements in deep learning technologies can be utilized to improve automated prediction accuracy further and (ii) the chemical indexing task is substantially more challenging. We look forward to further developing biomedical text-mining methods to respond to the rapid growth of biomedical literature. The NLM-Chem track dataset and other challenge materials are publicly available at https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/BC7-NLM-Chem-track/.

A SNPshot of PubMed to associate genetic variants with drugs, diseases, and adverse reactions.

MOTIVATION: Genetic factors determine differences in pharmacokinetics, drug efficacy, and drug responses between individuals and sub-populations. Wrong dosages of drugs can lead to severe adverse drug reactions in individuals whose drug metabolism drastically differs from the "assumed average". Databases such as PharmGKB are excellent sources of pharmacogenetic information on enzymes, genetic variants, and drug response affected by changes in enzymatic activity. Here, we seek to aid researchers, database curators, and clinicians in their search for relevant information by automatically extracting these data from literature. APPROACH: We automatically populate a repository of information on genetic variants, relations to drugs, occurrence in sub-populations, and associations with disease. We mine textual data from PubMed abstracts to discover such genotype-phenotype associations, focusing on SNPs that can be associated with variations in drug response. The overall repository covers relations found between genes, variants, alleles, drugs, diseases, adverse drug reactions, populations, and allele frequencies. We cross-reference these data to EntrezGene, PharmGKB, PubChem, and others. RESULTS: The performance regarding entity recognition and relation extraction yields a precision of 90-92% for the major entity types (gene, drug, disease), and 76-84% for relations involving these types. Comparison of our repository to PharmGKB reveals a coverage of 93% of gene-drug associations in PharmGKB and 97% of the gene-variant mappings based on 180,000 PubMed abstracts. AVAILABILITY: http://bioai4core.fulton.asu.edu/snpshot.

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/.