Deep learning-based enhancement of epigenomics data with AtacWorks
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DOI: 10.1038/s41467-021-21765-5
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Cited by:
- Zhiyuan Luo & Jiacheng Zhang & Jingyi Fei & Shengdong Ke, 2022. "Deep learning modeling m6A deposition reveals the importance of downstream cis-element sequences," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
- Eloise Berson & Anjali Sreenivas & Thanaphong Phongpreecha & Amalia Perna & Fiorella C. Grandi & Lei Xue & Neal G. Ravindra & Neelufar Payrovnaziri & Samson Mataraso & Yeasul Kim & Camilo Espinosa & A, 2023. "Whole genome deconvolution unveils Alzheimer’s resilient epigenetic signature," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
- Zhijian Li & Christoph Kuppe & Susanne Ziegler & Mingbo Cheng & Nazanin Kabgani & Sylvia Menzel & Martin Zenke & Rafael Kramann & Ivan G. Costa, 2021. "Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
- Samir Rachid Zaim & Mark-Phillip Pebworth & Imran McGrath & Lauren Okada & Morgan Weiss & Julian Reading & Julie L. Czartoski & Troy R. Torgerson & M. Juliana McElrath & Thomas F. Bumol & Peter J. Ske, 2024. "MOCHA’s advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts," Nature Communications, Nature, vol. 15(1), pages 1-24, December.
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