ArticleNature communications2018
Deep learning to predict the lab-of-origin of engineered DNA.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
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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
35 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
- Diagnosing soil bioremediation failure: evidence-weighted pathways from mechanism to field closure.Frontiers in bioengineering and biotechnology · 2026Review
- From Sequence to Response: AI-Guided Prediction of Nucleic Acid Nanoparticles Immune Recognitions.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Unlocking the potential: multimodal AI in biotechnology and digital medicine-economic impact and ethical challenges.NPJ digital medicine · 2025Review
- Engineering biology applications for environmental solutions: potential and challenges.Nature communications · 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
- Synsor: a tool for alignment-free detection of engineered DNA sequences.Frontiers in bioengineering and biotechnology · 2024Article
- Identifying widespread and recurrent variants of genetic parts to improve annotation of engineered DNA sequences.PloS one · 2024Article
- DEEP-squared: deep learning powered De-scattering with Excitation Patterning.Light, science & applications · 2023Article
- De-scattering Deep Neural Network Enables Fast Imaging of Spines through Scattering Media by Temporal Focusing Microscopy.Research square · 2023Article
- RNAdegformer: accurate prediction of mRNA degradation at nucleotide resolution with deep learning.Briefings in bioinformatics · 2023Article
- Engineering an AI-based forward-reverse platform for the design of cross-ribosome binding sites of a transcription factor biosensor.Computational and structural biotechnology journal · 2023Article
- Analysis of the first genetic engineering attribution challenge.Nature communications · 2022Article
- Artificial Immune Cell, AI-cell, a New Tool to Predict Interferon Production by Peripheral Blood Monocytes in Response to Nucleic Acid Nanoparticles.Small (Weinheim an der Bergstrasse, Germany) · 2022Article
- Omics Data and Data Representations for Deep Learning-Based Predictive Modeling.International journal of molecular sciences · 2022Review
- Current progress and open challenges for applying deep learning across the biosciences.Nature communications · 2022Review
- Ten quick tips for deep learning in biology.PLoS computational biology · 2022Article
- Genomics enters the deep learning era.PeerJ · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Genetic engineering projects are rapidly growing in scale and complexity, driven by new tools to design and construct DNA. There is increasing concern that widened access to these technologies could lead to attempts to construct cells for malicious intent, illegal drug production, or to steal intellectual property. Determining the origin of a DNA sequence is difficult and time-consuming. Here deep learning is applied to predict the lab-of-origin of a DNA sequence. A convolutional neural network was trained on the Addgene plasmid dataset that contained 42,364 engineered DNA sequences from 2230 labs as of February 2016. The network correctly identifies the source lab 48% of the time and 70% it appears in the top 10 predicted labs. Often, there is not a single "smoking gun" that affiliates a DNA sequence with a lab. Rather, it is a combination of design choices that are individually common but collectively reveal the designer.
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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.