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Knowledge-driven geospatial location resolution for phylogeographic models of virus migration
Davy Weissenbacher1, Tasnia Tahsin1, Rachel Beard2
1Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA and Center for Environmental Security, Biodesign Institute, Arizona State University, Tempe, AZ 85287-5904, USA.
Bioinformatics (Oxford, England)
|June 15, 2015
Summary
This study introduces an automated system for identifying and clarifying geographic locations in scientific articles, improving the metadata for zoonotic virus surveillance. The system enhances phylogeographic analysis by accurately extracting location data from research papers.
Area of Science:
- * Public Health and Epidemiology
- * Bioinformatics and Computational Biology
- * Virology and Zoonotic Disease Research
Background:
- * Zoonotic viral diseases pose a significant global public health risk.
- * Phylogeography aids in surveillance by analyzing virus migration and mutation patterns.
- * Accurate geospatial metadata of viral sequences is crucial for phylogeographic analysis.
Purpose of the Study:
- * To develop and evaluate an automated system for toponym resolution (location detection and disambiguation) in full-text scientific articles.
- * To improve the retrieval of geospatial metadata for viral sequences, aiding phylogeographic studies.
- * To enhance the efficiency and accuracy of data collection for zoonotic virus surveillance.
Main Methods:
- * Development of a system for automated detection and disambiguation of locations within full-text articles.
- * Testing the system on a manually annotated corpus of phylogeography-related journal articles.
- * Utilizing integrated heuristics for location disambiguation: distance, population, and a novel metadata heuristic leveraging GenBank data.
Main Results:
- * The system achieved the best performance using the metadata heuristic, with 0.54 Precision, 0.89 Recall, and 0.68 F-score for location detection and disambiguation.
- * Precision for location name disambiguation alone reached 0.88.
- * Error analysis indicated that improving geospatial location detection could further increase toponym resolution accuracy.
Conclusions:
- * The developed automated system is a valuable resource for phylogeographers needing accurate geospatial metadata.
- * Improved toponym resolution directly supports more effective surveillance of zoonotic viruses.
- * The system streamlines the process of gathering essential location data, overcoming limitations in public databases.
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