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Machine Learning for Characterization of Insect Vector Feeding

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  • Denis S Willett
  • Justin George
  • Nora S Willett
  • Lukasz L Stelinski
  • Stephen L Lapointe

Abstract

Insects that feed by ingesting plant and animal fluids cause devastating damage to humans, livestock, and agriculture worldwide, primarily by transmitting pathogens of plants and animals. The feeding processes required for successful pathogen transmission by sucking insects can be recorded by monitoring voltage changes across an insect-food source feeding circuit. The output from such monitoring has traditionally been examined manually, a slow and onerous process. We taught a computer program to automatically classify previously described insect feeding patterns involved in transmission of the pathogen causing citrus greening disease. We also show how such analysis contributes to discovery of previously unrecognized feeding states and can be used to characterize plant resistance mechanisms. This advance greatly reduces the time and effort required to analyze insect feeding, and should facilitate developing, screening, and testing of novel intervention strategies to disrupt pathogen transmission affecting agriculture, livestock and human health.Author Summary: Insect vectors acquire and transmit pathogens causing infectious diseases through probing on host tissues and ingesting host fluids. By connecting insects and their food source via an electrical circuit, computers, using machine learning algorithms, can learn to recognize insect feeding patterns involved in pathogen transmission. In addition, these machine learning algorithms can show us novel patterns of insect feeding and uncover mechanisms that lead to disruption of pathogen transmission. While we use these techniques to help save the citrus industry from a major decline due to an insect-transmitted bacterial pathogen, such intelligent monitoring of insect vector feeding will engender advances in disrupting transmission of pathogens causing disease in agriculture, livestock, and human health.

Suggested Citation

  • Denis S Willett & Justin George & Nora S Willett & Lukasz L Stelinski & Stephen L Lapointe, 2016. "Machine Learning for Characterization of Insect Vector Feeding," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-14, November.
    • Handle: RePEc:plo:pcbi00:1005158
    DOI: 10.1371/journal.pcbi.1005158
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    References listed on IDEAS

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    1. Visser, Ingmar & Speekenbrink, Maarten, 2010. "depmixS4: An R Package for Hidden Markov Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i07).
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