ArticleNature communications2026
Genome-guided generative adversarial learning enables nanopore adaptive sequencing.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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Who cites it
1 citing paper in PubMed.
- Genome-guided generative adversarial learning enables nanopore adaptive sequencing.Nature communications · 2026Article
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Nanopore adaptive sequencing enables real-time target enrichment, yet current deep-learning methods require costly, sample-specific experimental training data. To address this, we developed GANBase, a genome-guided generative adversarial learning framework, which is trained exclusively on reference sequences and incorporates a Monte Carlo Tree Search-based Rollout strategy for model training. GANBase demonstrates robust performance in target enrichment and host depletion across diverse scenarios. In live adaptive sequencing experiments, it remains effective despite significant pore loss or flow cell version updates, providing a data-independent solution that significantly expands the utility of real-time targeted sequencing.
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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.