Database Curation Research Articles

Effective biomedical document classification for identifying publications relevant to the mouse Gene Expression Database (GXD)

The Gene Expression Database (GXD) is a comprehensive online database within the Mouse Genome Informatics resource, aiming to provide available information about endogenous gene expression during mouse development. The information stems primarily from many thousands of biomedical publications that database curators must go through and read. Given the very large number of biomedical papers published each year, automatic document classification plays an important role in biomedical research. Specifically, an effective and efficient document classifier is needed for supporting the GXD annotation workflow. We present here an effective yet relatively simple classification scheme, which uses readily available tools while employing feature selection, aiming to assist curators in identifying publications relevant to GXD. We examine the performance of our method over a large manually curated dataset, consisting of more than 25 000 PubMed abstracts, of which about half are curated as relevant to GXD while the other half as irrelevant to GXD. In addition to text from title-and-abstract, we also consider image captions, an important information source that we integrate into our method. We apply a captions-based classifier to a subset of about 3300 documents, for which the full text of the curated articles is available. The results demonstrate that our proposed approach is robust and effectively addresses the GXD document classification. Moreover, using information obtained from image captions clearly improves performance, compared to title and abstract alone, affirming the utility of image captions as a substantial evidence source for automatically determining the relevance of biomedical publications to a specific subject area. Database url www.informatics.jax.org.

A text mining-based approach to graph database curation in support of metabolic pathway reconstruction

A text mining-based approach to graph database curation in support of metabolic pathway reconstruction

The Israeli National Genetic database: a 10-year experience

BackgroundThe Israeli National and Ethnic Mutation database (http://server.goldenhelix.org/israeli) was launched in September 2006 on the ETHNOS software to include clinically relevant genomic variants reported among Jewish and Arab Israeli patients. In 2016, the database was reviewed and corrected according to ClinVar (https://www.ncbi.nlm.nih.gov/clinvar) and ExAC (http://exac.broadinstitute.org) database entries. The present article summarizes some key aspects from the development and continuous update of the database over a 10-year period, which could serve as a paradigm of successful database curation for other similar resources.ResultsIn September 2016, there were 2444 entries in the database, 890 among Jews, 1376 among Israeli Arabs, and 178 entries among Palestinian Arabs, corresponding to an ~4× data content increase compared to when originally launched. While the Israeli Arab population is much smaller than the Jewish population, the number of pathogenic variants causing recessive disorders reported in the database is higher among Arabs (934) than among Jews (648). Nevertheless, the number of pathogenic variants classified as founder mutations in the database is smaller among Arabs (175) than among Jews (192). In 2016, the entire database content was compared to that of other databases such as ClinVar and ExAC. We show that a significant difference in the percentage of pathogenic variants from the Israeli genetic database that were present in ExAC was observed between the Jewish population (31.8%) and the Israeli Arab population (20.6%).ConclusionsThe Israeli genetic database was launched in 2006 on the ETHNOS software and is available online ever since. It allows querying the database according to the disorder and the ethnicity; however, many other features are not available, in particular the possibility to search according to the name of the gene. In addition, due to the technical limitations of the previous ETHNOS software, new features and data are not included in the present online version of the database and upgrade is currently ongoing.

Rapid development of entity-based data models for bioinformatics with persistence object-oriented design and structured interfaces

Databases are imperative for research in bioinformatics and computational biology. Current challenges in database design include data heterogeneity and context-dependent interconnections between data entities. These challenges drove the development of unified data interfaces and specialized databases. The curation of specialized databases is an ever-growing challenge due to the introduction of new data sources and the emergence of new relational connections between established datasets. Here, an open-source framework for the curation of specialized databases is proposed. The framework supports user-designed models of data encapsulation, objects persistency and structured interfaces to local and external data sources such as MalaCards, Biomodels and the National Centre for Biotechnology Information (NCBI) databases. The proposed framework was implemented using Java as the development environment, EclipseLink as the data persistency agent and Apache Derby as the database manager. Syntactic analysis was based on J3D, jsoup, Apache Commons and w3c.dom open libraries. Finally, a construction of a specialized database for aneurysms associated vascular diseases is demonstrated. This database contains 3-dimensional geometries of aneurysms, patient’s clinical information, articles, biological models, related diseases and our recently published model of aneurysms’ risk of rapture. Framework is available in: http://nbel-lab.com.

Effective biomedical document classification for identifying publications relevant to the mouse Gene Expression Database (GXD).

The Gene Expression Database (GXD) is a comprehensive online database within the Mouse Genome Informatics resource, aiming to provide available information about endogenous gene expression during mouse development. The information stems primarily from many thousands of biomedical publications that database curators must go through and read. Given the very large number of biomedical papers published each year, automatic document classification plays an important role in biomedical research. Specifically, an effective and efficient document classifier is needed for supporting the GXD annotation workflow. We present here an effective yet relatively simple classification scheme, which uses readily available tools while employing feature selection, aiming to assist curators in identifying publications relevant to GXD. We examine the performance of our method over a large manually curated dataset, consisting of more than 25 000 PubMed abstracts, of which about half are curated as relevant to GXD while the other half as irrelevant to GXD. In addition to text from title-and-abstract, we also consider image captions, an important information source that we integrate into our method. We apply a captions-based classifier to a subset of about 3300 documents, for which the full text of the curated articles is available. The results demonstrate that our proposed approach is robust and effectively addresses the GXD document classification. Moreover, using information obtained from image captions clearly improves performance, compared to title and abstract alone, affirming the utility of image captions as a substantial evidence source for automatically determining the relevance of biomedical publications to a specific subject area. Database URL: www.informatics.jax.org

SciLite: a platform for displaying text-mined annotations as a means to link research articles with biological data

Biological databases are fundamental to biological research and discovery. Database curation adds highly precise and useful information, usually extracted from the literature through experts reading research articles. The significant amount of time and effort put in by curators, against the backdrop of tremendous data growth, makes manual curation a high value task. Therefore, there is an urgent need to find ways to scale curation efforts by improving data integration, linking literature to the underlying data. As part of the development of Europe PMC, we have developed a new platform, SciLite, that overlays text-mined annotations on research articles. The aim is to aid Europe PMC users in finding key concepts more easily and provide links to related resources or tools, bridging the gap between literature and biological data.

Text Mining Genotype-Phenotype Relationships from Biomedical Literature for Database Curation and Precision Medicine.

The practice of precision medicine will ultimately require databases of genes and mutations for healthcare providers to reference in order to understand the clinical implications of each patient’s genetic makeup. Although the highest quality databases require manual curation, text mining tools can facilitate the curation process, increasing accuracy, coverage, and productivity. However, to date there are no available text mining tools that offer high-accuracy performance for extracting such triplets from biomedical literature. In this paper we propose a high-performance machine learning approach to automate the extraction of disease-gene-variant triplets from biomedical literature. Our approach is unique because we identify the genes and protein products associated with each mutation from not just the local text content, but from a global context as well (from the Internet and from all literature in PubMed). Our approach also incorporates protein sequence validation and disease association using a novel text-mining-based machine learning approach. We extract disease-gene-variant triplets from all abstracts in PubMed related to a set of ten important diseases (breast cancer, prostate cancer, pancreatic cancer, lung cancer, acute myeloid leukemia, Alzheimer’s disease, hemochromatosis, age-related macular degeneration (AMD), diabetes mellitus, and cystic fibrosis). We then evaluate our approach in two ways: (1) a direct comparison with the state of the art using benchmark datasets; (2) a validation study comparing the results of our approach with entries in a popular human-curated database (UniProt) for each of the previously mentioned diseases. In the benchmark comparison, our full approach achieves a 28% improvement in F1-measure (from 0.62 to 0.79) over the state-of-the-art results. For the validation study with UniProt Knowledgebase (KB), we present a thorough analysis of the results and errors. Across all diseases, our approach returned 272 triplets (disease-gene-variant) that overlapped with entries in UniProt and 5,384 triplets without overlap in UniProt. Analysis of the overlapping triplets and of a stratified sample of the non-overlapping triplets revealed accuracies of 93% and 80% for the respective categories (cumulative accuracy, 77%). We conclude that our process represents an important and broadly applicable improvement to the state of the art for curation of disease-gene-variant relationships.

Recognition of side effects as implicit-opinion words in drug reviews

Purpose Many opinion-mining systems and tools have been developed to provide users with the attitudes of people toward entities and their attributes or the overall polarities of documents. In addition, side effects are one of the critical measures used to evaluate a patient’s opinion for a particular drug. However, side effect recognition is a challenging task, since side effects coincide with disease symptoms lexically and syntactically. The purpose of this paper is to extract drug side effects from drug reviews as an integral implicit-opinion words. Design/methodology/approach This paper proposes a detection algorithm to a medical-opinion-mining system using rule-based and support vector machines (SVM) algorithms. A corpus from 225 drug reviews was manually annotated by a medical expert for training and testing. Findings The results show that SVM significantly outperforms a rule-based algorithm. However, the results of both algorithms are encouraging and a good foundation for future research. Obviating the limitations and exploiting combined approaches would improve the results. Practical implications An automatic extraction for adverse drug effects information from online text can help regulatory authorities in rapid information screening and extraction instead of manual inspection and contributes to the acceleration of medical decision support and safety alert generation. Originality/value The results of this study can help database curators in compiling adverse drug effects databases and researchers to digest the huge amount of textual online information which is growing rapidly.

Text Mining to Support Gene Ontology Curation and Vice Versa.

In this chapter, we explain how text mining can support the curation of molecular biology databases dealing with protein functions. We also show how curated data can play a disruptive role in the developments of text mining methods. We review a decade of efforts to improve the automatic assignment of Gene Ontology (GO) descriptors, the reference ontology for the characterization of genes and gene products. To illustrate the high potential of this approach, we compare the performances of an automatic text categorizer and show a large improvement of +225 % in both precision and recall on benchmarked data. We argue that automatic text categorization functions can ultimately be embedded into a Question-Answering (QA) system to answer questions related to protein functions. Because GO descriptors can be relatively long and specific, traditional QA systems cannot answer such questions. A new type of QA system, so-called Deep QA which uses machine learning methods trained with curated contents, is thus emerging. Finally, future advances of text mining instruments are directly dependent on the availability of high-quality annotated contents at every curation step. Databases workflows must start recording explicitly all the data they curate and ideally also some of the data they do not curate.

Filtering large-scale event collections using a combination of supervised and unsupervised learning for event trigger classification.

BackgroundBiomedical event extraction is one of the key tasks in biomedical text mining, supporting various applications such as database curation and hypothesis generation. Several systems, some of which have been applied at a large scale, have been introduced to solve this task.Past studies have shown that the identification of the phrases describing biological processes, also known as trigger detection, is a crucial part of event extraction, and notable overall performance gains can be obtained by solely focusing on this sub-task. In this paper we propose a novel approach for filtering falsely identified triggers from large-scale event databases, thus improving the quality of knowledge extraction.MethodsOur method relies on state-of-the-art word embeddings, event statistics gathered from the whole biomedical literature, and both supervised and unsupervised machine learning techniques. We focus on EVEX, an event database covering the whole PubMed and PubMed Central Open Access literature containing more than 40 million extracted events. The top most frequent EVEX trigger words are hierarchically clustered, and the resulting cluster tree is pruned to identify words that can never act as triggers regardless of their context. For rarely occurring trigger words we introduce a supervised approach trained on the combination of trigger word classification produced by the unsupervised clustering method and manual annotation.ResultsThe method is evaluated on the official test set of BioNLP Shared Task on Event Extraction. The evaluation shows that the method can be used to improve the performance of the state-of-the-art event extraction systems. This successful effort also translates into removing 1,338,075 of potentially incorrect events from EVEX, thus greatly improving the quality of the data. The method is not solely bound to the EVEX resource and can be thus used to improve the quality of any event extraction system or database.AvailabilityThe data and source code for this work are available at: http://bionlp-www.utu.fi/trigger-clustering/.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-016-0070-4) contains supplementary material, which is available to authorized users.

MiRiaD: A Text Mining Tool for Detecting Associations of microRNAs with Diseases.

BackgroundMicroRNAs are increasingly being appreciated as critical players in human diseases, and questions concerning the role of microRNAs arise in many areas of biomedical research. There are several manually curated databases of microRNA-disease associations gathered from the biomedical literature; however, it is difficult for curators of these databases to keep up with the explosion of publications in the microRNA-disease field. Moreover, automated literature mining tools that assist manual curation of microRNA-disease associations currently capture only one microRNA property (expression) in the context of one disease (cancer). Thus, there is a clear need to develop more sophisticated automated literature mining tools that capture a variety of microRNA properties and relations in the context of multiple diseases to provide researchers with fast access to the most recent published information and to streamline and accelerate manual curation.MethodsWe have developed miRiaD (microRNAs in association with Disease), a text-mining tool that automatically extracts associations between microRNAs and diseases from the literature. These associations are often not directly linked, and the intermediate relations are often highly informative for the biomedical researcher. Thus, miRiaD extracts the miR-disease pairs together with an explanation for their association. We also developed a procedure that assigns scores to sentences, marking their informativeness, based on the microRNA-disease relation observed within the sentence.ResultsmiRiaD was applied to the entire Medline corpus, identifying 8301 PMIDs with miR-disease associations. These abstracts and the miR-disease associations are available for browsing at http://biotm.cis.udel.edu/miRiaD. We evaluated the recall and precision of miRiaD with respect to information of high interest to public microRNA-disease database curators (expression and target gene associations), obtaining a recall of 88.46–90.78. When we expanded the evaluation to include sentences with a wide range of microRNA-disease information that may be of interest to biomedical researchers, miRiaD also performed very well with a F-score of 89.4. The informativeness ranking of sentences was evaluated in terms of nDCG (0.977) and correlation metrics (0.678-0.727) when compared to an annotator’s ranked list.ConclusionsmiRiaD, a high performance system that can capture a wide variety of microRNA-disease related information, extends beyond the scope of existing microRNA-disease resources. It can be incorporated into manual curation pipelines and serve as a resource for biomedical researchers interested in the role of microRNAs in disease. In our ongoing work we are developing an improved miRiaD web interface that will facilitate complex queries about microRNA-disease relationships, such as “In what diseases does microRNA regulation of apoptosis play a role?” or “Is there overlap in the sets of genes targeted by microRNAs in different types of dementia?”.”Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-015-0044-y) contains supplementary material, which is available to authorized users.

Text mining for precision medicine: automating disease-mutation relationship extraction from biomedical literature.

Identifying disease-mutation relationships is a significant challenge in the advancement of precision medicine. The aim of this work is to design a tool that automates the extraction of disease-related mutations from biomedical text to advance database curation for the support of precision medicine. We developed a machine-learning (ML) based method to automatically identify the mutations mentioned in the biomedical literature related to a particular disease. In order to predict a relationship between the mutation and the target disease, several features, such as statistical features, distance features, and sentiment features, were constructed. Our ML model was trained with a pre-labeled dataset consisting of manually curated information about mutation-disease associations. The model was subsequently used to extract disease-related mutations from larger biomedical literature corpora. The performance of the proposed approach was assessed using a benchmarking dataset. Results show that our proposed approach gains significant improvement over the previous state of the art and obtains F-measures of 0.880 and 0.845 for prostate and breast cancer mutations, respectively. To demonstrate its utility, we applied our approach to all abstracts in PubMed for 3 diseases (including a non-cancer disease). The mutations extracted were then manually validated against human-curated databases. The validation results show that the proposed approach is useful in a real-world setting to extract uncurated disease mutations from the biomedical literature. The proposed approach improves the state of the art for mutation-disease extraction from text. It is scalable and generalizable to identify mutations for any disease at a PubMed scale.

Intelligent Retrieval for Biodiversity

A proposal for intelligent retrieval in the biodiversity domain is described. It applies natural language processing to integrate linguistic and domain knowledge in a mathematical model for information management, formalizing the notion of semantic similarity in different degrees. The goal is to provide computational tools to identify, extract and relate not only data but also scientific notions, even if the information available to start the process is not complete. The use of conceptual graphs as a basis for interpretation makes it possible to avoid the use of classic ontologies, whose start-up requires costly generation and maintenance protocols and also unnecessarily overload the accessing task for inexpert users. We exploit the automatic generation of these structures from raw texts through graphical and natural language interaction, at the same time providing a solid logical and linguistic foundation to sustain the curation of databases.

Discovering biomedical semantic relations in PubMed queries for information retrieval and database curation.

Identifying relevant papers from the literature is a common task in biocuration. Most current biomedical literature search systems primarily rely on matching user keywords. Semantic search, on the other hand, seeks to improve search accuracy by understanding the entities and contextual relations in user keywords. However, past research has mostly focused on semantically identifying biological entities (e.g. chemicals, diseases and genes) with little effort on discovering semantic relations. In this work, we aim to discover biomedical semantic relations in PubMed queries in an automated and unsupervised fashion. Specifically, we focus on extracting and understanding the contextual information (or context patterns) that is used by PubMed users to represent semantic relations between entities such as ‘CHEMICAL-1 compared to CHEMICAL-2.’ With the advances in automatic named entity recognition, we first tag entities in PubMed queries and then use tagged entities as knowledge to recognize pattern semantics. More specifically, we transform PubMed queries into context patterns involving participating entities, which are subsequently projected to latent topics via latent semantic analysis (LSA) to avoid the data sparseness and specificity issues. Finally, we mine semantically similar contextual patterns or semantic relations based on LSA topic distributions. Our two separate evaluation experiments of chemical-chemical (CC) and chemical–disease (CD) relations show that the proposed approach significantly outperforms a baseline method, which simply measures pattern semantics by similarity in participating entities. The highest performance achieved by our approach is nearly 0.9 and 0.85 respectively for the CC and CD task when compared against the ground truth in terms of normalized discounted cumulative gain (nDCG), a standard measure of ranking quality. These results suggest that our approach can effectively identify and return related semantic patterns in a ranked order covering diverse bio-entity relations. To assess the potential utility of our automated top-ranked patterns of a given relation in semantic search, we performed a pilot study on frequently sought semantic relations in PubMed and observed improved literature retrieval effectiveness based on post-hoc human relevance evaluation. Further investigation in larger tests and in real-world scenarios is warranted.

NTTMUNSW BioC modules for recognizing and normalizing species and gene/protein mentions

In recent years, the number of published biomedical articles has increased as researchers have focused on biological domains to investigate the functions of biological objects, such as genes and proteins. However, the ambiguous nature of genes and their products have rendered the literature more complex for readers and curators of molecular interaction databases. To address this challenge, a normalization technique that can link variants of biological objects to a single, standardized form was applied. In this work, we developed a species normalization module, which recognizes species names and normalizes them to NCBI Taxonomy IDs. Unlike most previous work, which ignored the prefix of a gene name that represents an abbreviation of the species name to which the gene belongs, the recognition results of our module include the prefixed species. The developed species normalization module achieved an overall F-score of 0.954 on an instance-level species normalization corpus. For gene normalization, two separate modules were respectively employed to recognize gene mentions and normalize those mentions to their Entrez Gene IDs by utilizing a multistage normalization algorithm developed for processing full-text articles. All of the developed modules are BioC-compatible .NET framework libraries and are publicly available from the NuGet gallery.Database URL: https://sites.google.com/site/hjdairesearch/Projects/isn-corpus

BioCreative V CDR task corpus: a resource for chemical disease relation extraction

Community-run, formal evaluations and manually annotated text corpora are critically important for advancing biomedical text-mining research. Recently in BioCreative V, a new challenge was organized for the tasks of disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction. Given the nature of both tasks, a test collection is required to contain both disease/chemical annotations and relation annotations in the same set of articles. Despite previous efforts in biomedical corpus construction, none was found to be sufficient for the task. Thus, we developed our own corpus called BC5CDR during the challenge by inviting a team of Medical Subject Headings (MeSH) indexers for disease/chemical entity annotation and Comparative Toxicogenomics Database (CTD) curators for CID relation annotation. To ensure high annotation quality and productivity, detailed annotation guidelines and automatic annotation tools were provided. The resulting BC5CDR corpus consists of 1500 PubMed articles with 4409 annotated chemicals, 5818 diseases and 3116 chemical-disease interactions. Each entity annotation includes both the mention text spans and normalized concept identifiers, using MeSH as the controlled vocabulary. To ensure accuracy, the entities were first captured independently by two annotators followed by a consensus annotation: The average inter-annotator agreement (IAA) scores were 87.49% and 96.05% for the disease and chemicals, respectively, in the test set according to the Jaccard similarity coefficient. Our corpus was successfully used for the BioCreative V challenge tasks and should serve as a valuable resource for the text-mining research community.Database URL: http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/

NeXtA5: accelerating annotation of articles via automated approaches in neXtProt.

The rapid increase in the number of published articles poses a challenge for curated databases to remain up-to-date. To help the scientific community and database curators deal with this issue, we have developed an application, neXtA5, which prioritizes the literature for specific curation requirements. Our system, neXtA5, is a curation service composed of three main elements. The first component is a named-entity recognition module, which annotates MEDLINE over some predefined axes. This report focuses on three axes: Diseases, the Molecular Function and Biological Process sub-ontologies of the Gene Ontology (GO). The automatic annotations are then stored in a local database, BioMed, for each annotation axis. Additional entities such as species and chemical compounds are also identified. The second component is an existing search engine, which retrieves the most relevant MEDLINE records for any given query. The third component uses the content of BioMed to generate an axis-specific ranking, which takes into account the density of named-entities as stored in the Biomed database. The two ranked lists are ultimately merged using a linear combination, which has been specifically tuned to support the annotation of each axis. The fine-tuning of the coefficients is formally reported for each axis-driven search. Compared with PubMed, which is the system used by most curators, the improvement is the following: +231% for Diseases, +236% for Molecular Functions and +3153% for Biological Process when measuring the precision of the top-returned PMID (P0 or mean reciprocal rank). The current search methods significantly improve the search effectiveness of curators for three important curation axes. Further experiments are being performed to extend the curation types, in particular protein–protein interactions, which require specific relationship extraction capabilities. In parallel, user-friendly interfaces powered with a set of JSON web services are currently being implemented into the neXtProt annotation pipeline.Available on: http://babar.unige.ch:8082/neXtA5Database URL: http://babar.unige.ch:8082/neXtA5/fetcher.jsp

Crowd-sourcing and author submission as alternatives to professional curation.

Can we decrease the costs of database curation by crowd-sourcing curation work or by offloading curation to publication authors? This perspective considers the significant experience accumulated by the bioinformatics community with these two alternatives to professional curation in the last 20 years; that experience should be carefully considered when formulating new strategies for biological databases. The vast weight of empirical evidence to date suggests that crowd-sourced curation is not a successful model for biological databases. Multiple approaches to crowd-sourced curation have been attempted by multiple groups, and extremely low participation rates by ‘the crowd’ are the overwhelming outcome. The author-curation model shows more promise for boosting curator efficiency. However, its limitations include that the quality of author-submitted annotations is uncertain, the response rate is low (but significant), and to date author curation has involved relatively simple forms of annotation involving one or a few types of data. Furthermore, shifting curation to authors may simply redistribute costs rather than decreasing costs; author curation may in fact increase costs because of the overhead involved in having every curating author learn what professional curators know: curation conventions, curation software and curation procedures.

Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health.

The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next-generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text-found in biomedical publications and clinical notes-is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine.

Ambiguity of non-systematic chemical identifiers within and between small-molecule databases.

BackgroundA wide range of chemical compound databases are currently available for pharmaceutical research. To retrieve compound information, including structures, researchers can query these chemical databases using non-systematic identifiers. These are source-dependent identifiers (e.g., brand names, generic names), which are usually assigned to the compound at the point of registration. The correctness of non-systematic identifiers (i.e., whether an identifier matches the associated structure) can only be assessed manually, which is cumbersome, but it is possible to automatically check their ambiguity (i.e., whether an identifier matches more than one structure). In this study we have quantified the ambiguity of non-systematic identifiers within and between eight widely used chemical databases. We also studied the effect of chemical structure standardization on reducing the ambiguity of non-systematic identifiers.ResultsThe ambiguity of non-systematic identifiers within databases varied from 0.1 to 15.2 % (median 2.5 %). Standardization reduced the ambiguity only to a small extent for most databases. A wide range of ambiguity existed for non-systematic identifiers that are shared between databases (17.7–60.2 %, median of 40.3 %). Removing stereochemistry information provided the largest reduction in ambiguity across databases (median reduction 13.7 percentage points).ConclusionsAmbiguity of non-systematic identifiers within chemical databases is generally low, but ambiguity of non-systematic identifiers that are shared between databases, is high. Chemical structure standardization reduces the ambiguity to a limited extent. Our findings can help to improve database integration, curation, and maintenance.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0102-6) contains supplementary material, which is available to authorized users.