aceA machine learning framework for noise-driven DNA sequencing in solid-state nanopores
Solid-state nanopore devices have emerged as a promising platform for electrical signal based single molecule DNA sequencing with atomic-scale precision. However, stochastic noise in solid-state nanopores severely obscures signal quality, impeding nucleotide identification accuracy. Here, we have systematically examined the impact of noise on transverse tunnelling signals and their machine learning (ML) classification for nucleotide identification. The ML classification accuracy exhibited a non-monotonic relatio…
Graph neural networks applied to raw noisy tunnelling signals reached accuracy similar to denoised pipelines, giving a noise-resilient route to nucleotide identification in solid-state nanopore sequencing.
Stochastic noise in solid-state nanopores severely obscures signal quality and impedes the accuracy of nucleotide identification.
Evidence
- Peer-reviewedNanoscale2026-10-15
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Truvace Impact Record TRV-2026-1988, v1: “A machine learning framework for noise-driven DNA sequencing in solid-state nanopores.” Truvace, 2026-10-20. /record/TRV-2026-1988 (accessed at citation time). sha256 5d620db30612315b…
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