ArticleACS omega2026
CMA-Nano: A DNA Methylation Detection Method for Nanopore Sequencing Data Based on a Cross-Modal Attention Mechanism.
Article in ACS omega, 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Growth Phase-Related DNA Methylation and Localized DNA Fragment Density Variation inEpigenomes · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA methylation, particularly 5-methylcytosine (5mC), plays a pivotal role in gene regulation, development, and disease progression. Nanopore sequencing enables direct methylation detection without chemical treatment, but inherent signal noise and complex multimodal data pose analytical challenges. We present CMA-Nano, a deep learning framework that employs a cross-modal attention mechanism to integrate DNA sequences, nanopore signal statistics, and raw current signals, achieving effective fusion of multimodal features and capturing complex interdependencies. To further enhance feature representation and generalization, CMA-Nano leverages a masked base prediction pretraining strategy. Comprehensive evaluation on
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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.