Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks
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DOI: 10.1038/s41586-018-0289-6
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Cited by:
- Gabriel de Freitas Viscondi & Solange N. Alves-Souza, 2021. "Solar Irradiance Prediction with Machine Learning Algorithms: A Brazilian Case Study on Photovoltaic Electricity Generation," Energies, MDPI, vol. 14(18), pages 1-15, September.
- Karen Zhang & Yuan-Jyue Chen & Delaney Wilde & Kathryn Doroschak & Karin Strauss & Luis Ceze & Georg Seelig & Jeff Nivala, 2022. "A nanopore interface for higher bandwidth DNA computing," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
- Ilona Kulikovskikh & Sergej Prokhorov & Tomislav Lipić & Tarzan Legović & Tomislav Šmuc, 2019. "BioGD: Bio-inspired robust gradient descent," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-19, July.
- Kanakov, Oleg & Chen, Shangbin & Zaikin, Alexey, 2024. "Learning by selective plasmid loss for intracellular synthetic classifiers," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
- Linlin Yang & Qian Tang & Mingzhi Zhang & Yuan Tian & Xiaoxing Chen & Rui Xu & Qian Ma & Pei Guo & Chao Zhang & Da Han, 2024. "A spatially localized DNA linear classifier for cancer diagnosis," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
- Ahmed A. Agiza & Kady Oakley & Jacob K. Rosenstein & Brenda M. Rubenstein & Eunsuk Kim & Marc Riedel & Sherief Reda, 2023. "Digital circuits and neural networks based on acid-base chemistry implemented by robotic fluid handling," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
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