AIMC Topic: Nanopores

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Synthetic heparan sulfate standards and machine learning facilitate the development of solid-state nanopore analysis.

Proceedings of the National Academy of Sciences of the United States of America Mar 16, 2021
The application of solid-state (SS) nanopore devices to single-molecule nucleic acid sequencing has been challenging. Thus, the early successes in applying SS nanopore devices to the more difficult class of biopolymer, glycosaminoglycans (GAGs), have...
Machine Learning Heparitin Sulfate Monosaccharides Carbohydrate Sequence Disaccharides Glycomics Nanopores
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Deep learning for nanopore ionic current blockades.

The Journal of chemical physics Jan 28, 2021
DNA molecules can electrophoretically be driven through a nanoscale opening in a material, giving rise to rich and measurable ionic current blockades. In this work, we train machine learning models on experimental ionic blockade data from DNA nucleot...
Deep Learning DNA Molybdenum Ions Disulfides Nanopores
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Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning.

GigaScience May 1, 2018
Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex elec...
Neural Networks, Computer Machine Learning Software Reproducibility of Results Escherichia coli Sequence Analysis, DNA Signal Processing, Computer-Assisted Nucleotides Mycobacterium tuberculosis Probability Base Sequence Base Pairing Nanopores
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