Designing sensitive viral diagnostics with machine learning

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Abstract
Design of nucleic acid-based viral diagnostics typically follows heuristic rules and, to contend with viral variation, focuses on a genome’s conserved regions. A design process could, instead, directly optimize diagnostic effectiveness using a learned model of sensitivity for targets and their variants. Toward that goal, we screen 19,209 diagnostic–target pairs, concentrated on CRISPR-based diagnostics, and train a deep neural network to accurately predict diagnostic readout. We join this model with combinatorial optimization to maximize sensitivity over the full spectrum of a virus’s genomic variation. We introduce Activity-informed Design with All-inclusive Patrolling of Targets (ADAPT), a system for automated design, and use it to design diagnostics for 1,933 vertebrate-infecting viral species within 2 hours for most species and within 24 hours for all but three. We experimentally show that ADAPT’s designs are sensitive and specific to the lineage level and permit lower limits of detection, across a virus’s variation, than the outputs of standard design techniques. Our strategy could facilitate a proactive resource of assays for detecting pathogens.
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Funding Information
  • U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (K01 AI163498)
  • Amazon Web Services (Amazon Diagnostic Development Initiative)
  • U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (T32 GM008313)
  • Landry Cancer Biology Consortium Fellowship
  • National Science Foundation (CCF-1535795, CCF-1563710)
  • United States Department of Defense | Defense Advanced Research Projects Agency (D18AC00006)
  • Howard Hughes Medical Institute
  • (1) Flu Lab; (2) A cohort of generous donors through TED’s Audacious Project, including the ELMA Foundation, MacKenzie Scott, the Skoll Foundation, and Open Philanthropy

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