Tobin Magle, PhD
Health Sciences Library
University of Colorado Anschutz Medical Campus
Quetzal is a literature search engine that is tailored to the needs of biomedical researchers. Quetzal uses Quantum Logic Linguistic technology, a proprietary combination of linguistic and statistical algorithms, to search a variety of biomedical information sources such as MEDLINE, TOXLINE, biomedical news outlets, NIH grants, AHRQ guidelines, patents, and article full text. For a full list of indexed titles, see https://www.quetzal-search.info/pages/content.shtml.
The Quetzal interface is more intuitive for nonexpert searchers than the gold standard biomedical search engine, PubMed. The search fields for keywords, journals, authors, and affiliations/assignees are separate, eliminating cross talk between fields that can yield irrelevant results. Additionally, the linguistic algorithms allow for the use of “action words” (verbs). Entering two to three keywords in a sentence-like query and subsequently narrowing the results using filters after the search is performed enables an approach to searching similar to the exploratory search strategy that many biomedical researchers use.
The Quetzal ontology allows users to enter important concepts, including verbs, to simply retrieve relevant results by including synonyms. This manually curated ontology was built specifically for the biosciences. For example, a search for “smoking causes cancer” retrieves relevant statements that include synonyms for “causes” like “induces,” “alters,” and “is responsible for” as well as a variety of types of cancer as synonyms for “cancer.”
A major difference between Quetzal’s search interface and many others is that Quetzal does not use Boolean logic in the search queries. While this may seem like a shortcoming, the tailored ontology, linguistic technology, and comprehensive filters allow users to find what they need. Additionally, Boolean logic can be confusing for nonexpert searchers, and short, sentence-like queries in combination with the filters can be more intuitive for this demographic.
A key part of this biomedical ontology is the entity identification engine, which deciphers commonly used biomedical abbreviations. For example, nitric oxide (abbreviated NO) is a common concept in biomedical research, especially immunology. Quetzal recognizes the abbreviation “NO” and finds articles about nitric oxide, whereas PubMed sees NO as a stop term and returns 0 results. The entity identification engine also recognizes gene names, which is essential for finding appropriate biomedical research.
Users can also search Quetzal using power terms, which are generic names for categories of concepts. For example, searching for the power term $Disease will return results like “allergy,” “depression,” and “carcinoma,” but not results that merely contain the word “disease,” which is commonly found in the literature. Quetzal also includes a power term for $Genes, which returns papers that include specific gene names, but not just the word “genes.” Users can find a complete list of power terms at https://www.quetzal-search.info/pages/powerterms.shtml.
Because Quetzal operates best with a few search terms, filters are essential to narrowing the search results. Users can limit their searches by publication date and publication type, such as reviews, clinical trials, and AHRQ guidelines. They can also add extra search terms using the Also Containing filter, which allows users to specify whether to look for additional terms in the relevant statements or the entire text. Additionally, users can exclude concepts using the Not Containing filter.
One of the most impressive filters in Quetzal is the Key Concept filter, which is powered by the key concept identification engine. This engine identifies prevalent concepts within search results and generates a list of custom filters, including power terms, general concepts, and actions. For example, searching “pathogen causes $Disease” generates the key concept “bacteria,” allowing the user to narrow to a specific subset of pathogens.
Finally, the Negative Statement filter allows the user to limit to or exclude negative statements. This feature is useful for finding contradictory reports that could be buried in positive findings. For example, the search bacteria causes $Disease returns articles about disease-causing bacteria, but applying the negative statement filter teases out reports of diseases that are not typically caused by bacteria.
Quetzal searches return two lists of results: focused results, which are generated by Quetzal’s proprietary algorithm, and broader results, which are generated using a more standard keyword search. Focused results include relevant statements, which are sentences extracted from the text that include the information requested in the query. Both focused and broader results include contextual highlighting to show the user where the search terms are in the results. These features make it easier to skim the results for relevant literature.
Quetzal also includes a variety of productivity and collaboration tools. Journal club allows users to hold private conversations about research articles on the web. Users can also save searches and receive email alerts when new articles matching their search strategies are added. Quetzal also provides links to library holdings and direct access to PDFs at some subscription levels. Finally, Quetzal allows users to export citations in RIS or CSV format for use in citation management programs and sharing with collaborators.
Quetzal has three versions: Basic, Professional and Advanced. The features included in each version and the pricing are shown in the table below. Institutional pricing is available upon request. All users need an account to search Quetzal, even for the basic version, which does not require a subscription.
Biomedical NewsNIH grants
|All Basic Features
Private journal ClubSave search
Link to library holdings
Direct PDF access
Export results (.RIS, .CSV)
|AHRQ guidelines Patents
|All Professional features
Overall, Quetzal provides a more intuitive search experience for searchers who are not familiar with expert search concepts like controlled terminologies and Boolean logic. The unique combination of linguistic and statistical algorithms allows the use of action words in the search. This strategy also makes the negative statement filter possible, allowing the user to tease out hard to find negative statements. This search algorithm combined with a specific biomedical ontology allows a more intuitive experience for biomedical researchers. Additionally, the key concept filters that are tailored to the search results are excellent for discovering concepts users may not have considered relevant before conducting the search.