ArticleNature communications2021
Towards inferring nanopore sequencing ionic currents from nucleotide chemical structures.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Structure-informed models for ionic current prediction in nanopore sequencing of expanded dna alphabets.Nucleic acids research · 2025Article
- Leveraging basecaller's move table to generate a lightweight k-mer model for nanopore sequencing analysis.Bioinformatics (Oxford, England) · 2025Article
- Integration of Artificial Intelligence and Quantum Transport toward Stereoselective Identification of Carbohydrate Isomers.ACS central science · 2024Article
- Biosensors for Public Health and Environmental Monitoring: The Case for Sustainable Biosensing.ACS sustainable chemistry & engineering · 2024Review
- Article
- Towards inferring nanopore sequencing ionic currents from nucleotide chemical structures.Nature communications · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The characteristic ionic currents of nucleotide kmers are commonly used in analyzing nanopore sequencing readouts. We present a graph convolutional network-based deep learning framework for predicting kmer characteristic ionic currents from corresponding chemical structures. We show such a framework can generalize the chemical information of the 5-methyl group from thymine to cytosine by correctly predicting 5-methylcytosine-containing DNA 6mers, thus shedding light on the de novo detection of nucleotide modifications.
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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.