Dataset Information

Gaps within the Biomedical Literature: Initial Characterization and Assessment of Strategies for Discovery.

ABSTRACT: Within well-established fields of biomedical science, we identify "gaps", topical areas of investigation that might be expected to occur but are missing. We define a field by carrying out a topical PubMed query, and analyze Medical Subject Headings by which the set of retrieved articles are indexed. Medical Subject headings (MeSH terms) which occur in >1% of the articles are examined pairwise to see how often they are predicted to co-occur within individual articles (assuming that they are independent of each other). A pair of MeSH terms that are predicted to co-occur in at least 10 articles, yet are not observed to co-occur in any article, are "gaps" and were studied further in a corpus of 10 disease-related article sets and 10 related to biological processes. Overall, articles that filled gaps were cited more heavily than non-gap-filling articles and were 61% more likely to be published in multidisciplinary high-impact journals. Nine different features of these "gaps" were characterized and tested to learn which, if any, correlate with the appearance of one or more articles containing both MeSH terms within the next five years. Several different types of gaps were identified, each having distinct combinations of predictive features: a) those arising as a byproduct of MeSH indexing rules; b) those having little biological meaning; c) those representing "low hanging fruit" for immediate exploitation; and d) those representing gaps across disciplines or sub-disciplines that do not talk to each other or work together. We have built a free, open tool called "Mine the Gap!" that identifies and characterizes the "gaps" for any PubMed query, which can be accessed via the Anne O'Tate value-added PubMed search interface (http://arrowsmith.psych.uic.edu/cgi-bin/arrowsmith_uic/AnneOTate.cgi).

SUBMITTER: Peng Y

PROVIDER: S-EPMC5736374 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Gaps within the Biomedical Literature: Initial Characterization and Assessment of Strategies for Discovery.

Peng Yufang Y Bonifield Gary G Smalheiser Neil R NR

Frontiers in research metrics and analytics 20170522

Within well-established fields of biomedical science, we identify "gaps", topical areas of investigation that might be expected to occur but are missing. We define a field by carrying out a topical PubMed query, and analyze Medical Subject Headings by which the set of retrieved articles are indexed. Medical Subject headings (MeSH terms) which occur in >1% of the articles are examined pairwise to see how often they are predicted to co-occur within individual articles (assuming that they are indep ...[more]

PMID: 29271976

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Project description:BACKGROUND:With the substantial growth in the biomedical research literature, a larger number of claims are published daily, some of which seemingly disagree with or contradict prior claims on the same topics. Resolving such contradictions is critical to advancing our understanding of human disease and developing effective treatments. Automated text analysis techniques can facilitate such analysis by extracting claims from the literature, flagging those that are potentially contradictory, and identifying any study characteristics that may explain such contradictions. METHODS:Using SemMedDB, our own PubMed-scale repository of semantic predications (subject-relation-object triples), we identified apparent contradictions in the biomedical research literature and developed a categorization of contextual characteristics that explain such contradictions. Clinically relevant semantic predications relating to 20 diseases and involving opposing predicate pairs (e.g., an intervention treats or causes a disease) were retrieved from SemMedDB. After addressing inference, uncertainty, generic concepts, and NLP errors through automatic and manual filtering steps, a set of apparent contradictions were identified and characterized. RESULTS:We retrieved 117,676 predication instances from 62,360 PubMed abstracts (Jan 1980-Dec 2016). From these instances, automatic filtering steps generated 2236 candidate contradictory pairs. Through manual analysis, we determined that 58 of these pairs (2.6%) were apparent contradictions. We identified five main categories of contextual characteristics that explain these contradictions: (a) internal to the patient, (b) external to the patient, (c) endogenous/exogenous, (d) known controversy, and (e) contradictions in literature. Categories (a) and (b) were subcategorized further (e.g., species, dosage) and accounted for the bulk of the contradictory information. CONCLUSIONS:Semantic predications, by accounting for lexical variability, and SemMedDB, owing to its literature scale, can support identification and elucidation of potentially contradictory claims across the biomedical domain. Further filtering and classification steps are needed to distinguish among them the true contradictory claims. The ability to detect contradictions automatically can facilitate important biomedical knowledge management tasks, such as tracking and verifying scientific claims, summarizing research on a given topic, identifying knowledge gaps, and assessing evidence for systematic reviews, with potential benefits to the scientific community. Future work will focus on automating these steps for fully automatic recognition of contradictions from the biomedical research literature.

Project description:BackgroundFor selection and evaluation of potential biomarkers, inclusion of already published information is of utmost importance. In spite of significant advancements in text- and data-mining techniques, the vast knowledge space of biomarkers in biomedical text has remained unexplored. Existing named entity recognition approaches are not sufficiently selective for the retrieval of biomarker information from the literature. The purpose of this study was to identify textual features that enhance the effectiveness of biomarker information retrieval for different indication areas and diverse end user perspectives.MethodsA biomarker terminology was created and further organized into six concept classes. Performance of this terminology was optimized towards balanced selectivity and specificity. The information retrieval performance using the biomarker terminology was evaluated based on various combinations of the terminology's six classes. Further validation of these results was performed on two independent corpora representing two different neurodegenerative diseases.ResultsThe current state of the biomarker terminology contains 119 entity classes supported by 1890 different synonyms. The result of information retrieval shows improved retrieval rate of informative abstracts, which is achieved by including clinical management terms and evidence of gene/protein alterations (e.g. gene/protein expression status or certain polymorphisms) in combination with disease and gene name recognition. When additional filtering through other classes (e.g. diagnostic or prognostic methods) is applied, the typical high number of unspecific search results is significantly reduced. The evaluation results suggest that this approach enables the automated identification of biomarker information in the literature. A demo version of the search engine SCAIView, including the biomarker retrieval, is made available to the public through http://www.scaiview.com/scaiview-academia.html.ConclusionsThe approach presented in this paper demonstrates that using a dedicated biomarker terminology for automated analysis of the scientific literature maybe helpful as an aid to finding biomarker information in text. Successful extraction of candidate biomarkers information from published resources can be considered as the first step towards developing novel hypotheses. These hypotheses will be valuable for the early decision-making in the drug discovery and development process.

Dataset Information

Gaps within the Biomedical Literature: Initial Characterization and Assessment of Strategies for Discovery.

Publications

Gaps within the Biomedical Literature: Initial Characterization and Assessment of Strategies for Discovery.

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