Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data
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DOI: 10.1038/s41467-019-10168-2
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Cited by:
- Eseosa Halima Ighile & Hiroaki Shirakawa & Hiroki Tanikawa, 2022. "Application of GIS and Machine Learning to Predict Flood Areas in Nigeria," Sustainability, MDPI, vol. 14(9), pages 1-33, April.
- Dominik Stanojević & Zhe Li & Sara Bakić & Roger Foo & Mile Šikić, 2024. "Rockfish: A transformer-based model for accurate 5-methylcytosine prediction from nanopore sequencing," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
- Ziyuan Wang & Yinshan Fang & Ziyang Liu & Ning Hao & Hao Helen Zhang & Xiaoxiao Sun & Jianwen Que & Hongxu Ding, 2024. "Adapting nanopore sequencing basecalling models for modification detection via incremental learning and anomaly detection," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
- Abdur Rasool & Qiang Qu & Yang Wang & Qingshan Jiang, 2022. "Bio-Constrained Codes with Neural Network for Density-Based DNA Data Storage," Mathematics, MDPI, vol. 10(5), pages 1-21, March.
- Mian Umair Ahsan & Anagha Gouru & Joe Chan & Wanding Zhou & Kai Wang, 2024. "A signal processing and deep learning framework for methylation detection using Oxford Nanopore sequencing," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
- Gaolian Xu & Hao Yang & Jiani Qiu & Julien Reboud & Linqing Zhen & Wei Ren & Hong Xu & Jonathan M. Cooper & Hongchen Gu, 2023. "Sequence terminus dependent PCR for site-specific mutation and modification detection," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
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