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Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes

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Listed:
  • Jason Youn

    (University of California
    University of California
    University of California)

  • Navneet Rai

    (University of California
    University of California
    University of California)

  • Ilias Tagkopoulos

    (University of California
    University of California
    University of California)

Abstract

We present a machine learning framework to automate knowledge discovery through knowledge graph construction, inconsistency resolution, and iterative link prediction. By incorporating knowledge from 10 publicly available sources, we construct an Escherichia coli antibiotic resistance knowledge graph with 651,758 triples from 23 triple types after resolving 236 sets of inconsistencies. Iteratively applying link prediction to this graph and wet-lab validation of the generated hypotheses reveal 15 antibiotic resistant E. coli genes, with 6 of them never associated with antibiotic resistance for any microbe. Iterative link prediction leads to a performance improvement and more findings. The probability of positive findings highly correlates with experimentally validated findings (R2 = 0.94). We also identify 5 homologs in Salmonella enterica that are all validated to confer resistance to antibiotics. This work demonstrates how evidence-driven decisions are a step toward automating knowledge discovery with high confidence and accelerated pace, thereby substituting traditional time-consuming and expensive methods.

Suggested Citation

  • Jason Youn & Navneet Rai & Ilias Tagkopoulos, 2022. "Knowledge integration and decision support for accelerated discovery of antibiotic resistance genes," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29993-z
    DOI: 10.1038/s41467-022-29993-z
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    References listed on IDEAS

    as
    1. Danesh Moradigaravand & Martin Palm & Anne Farewell & Ville Mustonen & Jonas Warringer & Leopold Parts, 2018. "Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data," PLOS Computational Biology, Public Library of Science, vol. 14(12), pages 1-17, December.
    2. Shingo Suzuki & Takaaki Horinouchi & Chikara Furusawa, 2014. "Prediction of antibiotic resistance by gene expression profiles," Nature Communications, Nature, vol. 5(1), pages 1-12, December.
    3. Xiaokang Wang & Navneet Rai & Beatriz Merchel Piovesan Pereira & Ameen Eetemadi & Ilias Tagkopoulos, 2020. "Accelerated knowledge discovery from omics data by optimal experimental design," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    4. Vahe Tshitoyan & John Dagdelen & Leigh Weston & Alexander Dunn & Ziqin Rong & Olga Kononova & Kristin A. Persson & Gerbrand Ceder & Anubhav Jain, 2019. "Unsupervised word embeddings capture latent knowledge from materials science literature," Nature, Nature, vol. 571(7763), pages 95-98, July.
    5. Paul Raccuglia & Katherine C. Elbert & Philip D. F. Adler & Casey Falk & Malia B. Wenny & Aurelio Mollo & Matthias Zeller & Sorelle A. Friedler & Joshua Schrier & Alexander J. Norquist, 2016. "Machine-learning-assisted materials discovery using failed experiments," Nature, Nature, vol. 533(7601), pages 73-76, May.
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