Dug: A semantic search engine leveraging peer-reviewed knowledge to query biomedical data repositories

Rebecca Rosenblatt Boyles; Robert Franklin Chew; Virginia K. Hench; Alex M. Waldrop; Christopher Michael Ball; John Cheadle; Alexander Jackson Preiss; Jonathan Holt; Kira Caitlin Bradford; Alex Waldrop; John Cheadle; Kira Caitlin Bradford; Sandy Preiss; Rob Chew; Jonathan Holt; Yaphet Kebede; Nathan Braswell; M Watson; Ginnie Hench; Andrew Crerar; Chris Ball; Carl Schreep; PJ Linebaugh; Hannah Hiles; Rebecca Rosenblatt Boyles; Chris Bizon; Ashok Krishnamurthy; Steve Cox

Dug

A semantic search engine leveraging peer-reviewed knowledge to query biomedical data repositories

Waldrop, A., Cheadle, J., Bradford, K. C., Preiss, S., Chew, R., Holt, J., Kebede, Y., Braswell, N., Watson, M., Hench, G., Crerar, A., Ball, C., Schreep, C., Linebaugh, PJ., Hiles, H., Boyles, R. R., Bizon, C., Krishnamurthy, A., & Cox, S. (2022). Dug: A semantic search engine leveraging peer-reviewed knowledge to query biomedical data repositories. Bioinformatics, 38(12), 3252-3258. https://doi.org/10.1093/bioinformatics/btac284

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Abstract

Motivation: As the number of public data resources continues to proliferate, identifying relevant datasets across heterogenous repositories is becoming critical to answering scientific questions. To help researchers navigate this data landscape, we developed Dug: a semantic search tool for biomedical datasets utilizing evidence-based relationships from curated knowledge graphs to find relevant datasets and explain why those results are returned.Results: Developed through the National Heart, Lung and Blood Institute's (NHLBI) BioData Catalyst ecosystem, Dug has indexed more than 15 911 study variables from public datasets. On a manually curated search dataset, Dug's total recall (total relevant results/total results) of 0.79 outperformed default Elasticsearch's total recall of 0.76. When using synonyms or related concepts as search queries, Dug (0.36) far outperformed Elasticsearch (0.14) in terms of total recall with no significant loss in the precision of its top results.