Extracting geographic locations from the literature for virus phylogeography using supervised and distant supervision methods

Transcription

1 Extracting geographic locations from the literature for virus phylogeography using supervised and distant supervision methods D. Weissenbacher 1, A. Sarker 2, T. Tahsin 1, G. Gonzalez 2 and M. Scotch 1 1 Department of Biomedical Informatics, Arizona State University 2 Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania

2 Outline Phylogeography Geospatial insufficiency in Sequence Databases Automatic Toponyms Detection Evaluation of Toponym Detection Next Steps

3 Learning Objectives At the end of the presentation, the audience will be able to: Define phylogeography Explain the insufficiency of the geospatial metadata in the GenBank database Define the natural language processing task of toponyms resolution List the main methods for toponym detection Report on the current performances of toponym detection when applied to free-text journal articles

4 Phylogeography Like phylogenetics, but: TX TX CA CA AZ Geography is additional dimension RNA Viruses (zoonotic) Virus DNA sequences

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7 Genetic Sequence Databases GenBank, GISAID for flu, FLuDB, ViPR Contain virus sequences plus metadata: Virus Name Gene Host Location Collection Date

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9 Insufficient Metadata Majority of records have imprecise geospatial metadata (China, USA, France, etc.) [Scotch et al., 2011; Bioinformatics] What are the options? 1. Discard record from study 2. Use it anyway (change scope) 3. Find a more precise location in the corresponding journal article

10 Our Work: Resolve Toponyms in Publications Pubmed Central Article, PMC A H1N1 virus was isolated in 2009 from a child hospitalized in Nanjing. GenBank Organism = H1N1 Host = Homo Sapiens Country = China, Nanjing

11 Toponym Resolution Overview Assign unique coordinates to all names of places in documents [Speriosu, 2013; PhD thesis] Detection Disambiguation Here a H5N1 influenza virus was isolated from a sick pigeon in Paris in We determined the complete genomic? sequence with an ABI 3730 genetic analyzer using the Sanger method.

12 Objective Previous work, disambiguation is easy but not the detection [Weissenbacher et al. 2015; Bioinformatics] Detection and Disambiguation (end-to-end): 0.68 F-score Disambiguation only (toponyms are given): 0.94 F-score Detection only: 0.72 F-score Goal: improving the toponym detection

13 Toponym Detection Difficulties Toponyms ambiguous with named entities (e.g. Paris in France vs. Paris Hilton) Other English words (e.g. May in Russia vs. May ) Acronyms, Abbreviations, Misspellings Errors when pre-processing documents

14 Detection Methods Dictionary-based: find exact match with a lexicon (e.g. Geonames) Rule-based: manually defined (e.g. stop lists, preceding words, etc.) Machine Learning (ML): weighted features (Conditional Random Fields) Deep Learning: features learned automatically

15 Conditional Random Fields Classifier (CRF) Token True Class Predicted Class Previous Token Next Token The O O:0.98 <S> H3XXX Yes H3N2 O O:0.97 The virus Yes Predict the label of each token virus O O:0.78 H3XXX was No was O O:0.99 virus infecting No infecting O O:0.98 was swines No swines O O:0.76 infecting in No in O O:0.56 swines South No South B B:0.83 in Korea Yes Korea I I:0.97 South and Yes and O O:0.56 Korea China No China B O:0.51 and. Yes. O O:0.95 China </S> No IsCapitalized given a set of features

16 Predefined Features Lexical word to classify, its lexical properties, surrounding words, POS tags Knowledge-based Geonames mapping + stop list, MetaMap mapping (UMLS) Semantic clusters of similar words

17 Gold Standard 1,881 toponyms annotated 500,000 words 60 PubMed articles on influenza 5,730 GenBank records linked Inter-annotator agreement:.97p,.98r (16 articles)

18 Results Training: 1,596 toponyms (48 documents) Testing: 285 toponyms (12 documents) Precision Recall F-Score Rule-based Naïve Bayes (ML) CRF (ML)

19 CRF Learning Curve

20 Summary Toponym detector reaching state-of-the-art performances Existing toponym resolution improved Integration of our toponym resolver in our platform to complete missing geographic metadata in GenBank

21 Ongoing Work Distance supervision: use metadata to find sentences with toponyms Deep learning training a convolutional neural network to learn the features Phylogeographic evaluation of the contribution of more precise locations to phylogeography models (in progress)

22 Thank You! Funding: NIH/NIAID R01AI s: D. Weissenbacher: M. Scotch: G. Gonzalez: A. Sarker: Resources: