ArticleNature communications2020
A machine learning toolkit for genetic engineering attribution to facilitate biosecurity.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Resilience of recombinant antibiotic resistance gene-containing plasmids against common cell culture disposal methods.Cell reports methods · 2026Article
- PlasmidGPT: A generative framework for plasmid analysis and generation.Science advances · 2026Article
- Exo-Tox: Identifying Exotoxins from secreted bacterial proteins.BioData mining · 2025Article
- Responsible AI in biotechnology: balancing discovery, innovation and biosecurity risks.Frontiers in bioengineering and biotechnology · 2025Review
- The need for adaptability in detection, characterization, and attribution of biosecurity threats.Nature communications · 2024Review
- Cryptographic approaches to authenticating synthetic DNA sequences.Trends in biotechnology · 2024Review
- Artificial intelligence challenges in the face of biological threats: emerging catastrophic risks for public health.Frontiers in artificial intelligence · 2024Article
- Identifying widespread and recurrent variants of genetic parts to improve annotation of engineered DNA sequences.PloS one · 2024Article
- Synsor: a tool for alignment-free detection of engineered DNA sequences.Frontiers in bioengineering and biotechnology · 2024Article
- Analysis of the first genetic engineering attribution challenge.Nature communications · 2022Article
- Editorial: Recent advances in plant genetic engineering and innovative applications.Frontiers in plant science · 2022Article
- PlasmidHawk improves lab of origin prediction of engineered plasmids using sequence alignment.Nature communications · 2021Article
- The biosecurity benefits of genetic engineering attribution.Nature communications · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
The promise of biotechnology is tempered by its potential for accidental or deliberate misuse. Reliably identifying telltale signatures characteristic to different genetic designers, termed 'genetic engineering attribution', would deter misuse, yet is still considered unsolved. Here, we show that recurrent neural networks trained on DNA motifs and basic phenotype data can reach 70% attribution accuracy in distinguishing between over 1,300 labs. To make these models usable in practice, we introduce a framework for weighing predictions against other investigative evidence using calibration, and bring our model to within 1.6% of perfect calibration. Additionally, we demonstrate that simple models can accurately predict both the nation-state-of-origin and ancestor labs, forming the foundation of an integrated attribution toolkit which should promote responsible innovation and international security alike.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.