2022
DOI: 10.1038/s41587-022-01213-5
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Designing sensitive viral diagnostics with machine learning

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 combinator… Show more

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Cited by 53 publications
(83 citation statements)
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“…Using the resulting amplicon sequences, we then applied ADAPT ( 30 ) to design crRNA guides that optimally distinguish each species from all others. These guides were predicted not to cross-react (> 3 base pair mismatches) with amplicons corresponding to all included strains from the 51 nontarget pathogens in the panel.…”
Section: Resultsmentioning
confidence: 99%
“…Using the resulting amplicon sequences, we then applied ADAPT ( 30 ) to design crRNA guides that optimally distinguish each species from all others. These guides were predicted not to cross-react (> 3 base pair mismatches) with amplicons corresponding to all included strains from the 51 nontarget pathogens in the panel.…”
Section: Resultsmentioning
confidence: 99%
“…This included all viruses covered by BioFire RP2.1-SARS-CoV-2, four other human-associated coronaviruses and both influenza strains- as well as a few additional illness-inducing viruses 41 . To generate maximally active virus-specific crRNAs and PCR primers to detect the 21 viruses, we applied the assay design method ADAPT (Activity-informed Design with All-inclusive Patrolling of Targets; described in the Methods ) 35 . We were able to encompass the full genomic diversity of these viral families by including multiple primers, if needed.…”
Section: Resultsmentioning
confidence: 99%
“…The crRNAs for SNP discrimination were designed using a generative sequence design algorithm (Mantena, S. et al, manuscript in preparation). This approach uses ADAPT’s predictive model to predict the activity of candidate crRNA sequences against on-target and off-target sequences 35 . These predictions of candidate crRNA activity steer the generative algorithm’s optimization process, where it seeks to design crRNA probes that have maximal predicted on-target activity and minimal predicted off-target activity.…”
Section: Methodsmentioning
confidence: 99%
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“…Thus, we explored the possibility of using machine learning (ML) to build a prognostic model for CRC. ML is a branch of arti cial intelligence (AI) in which a computer generates rules underlying or based on raw data (Mitchell, 2003); the use of ML in medicine has gradually become common (Grimm et al 2022;Kim et al 2022;Metsky et al 2022; Xie and Zhuang and Niu and Ai et al 2022). ML can be used to directly compare the accuracy of two or more quantitative tests for the same disease/condition (Tripepi et al 2009).…”
Section: Introductionmentioning
confidence: 99%