Deep learning for biology
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DOI: 10.1038/d41586-018-02174-z
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Cited by:
- Minji Lee & Leandro R. D. Sanz & Alice Barra & Audrey Wolff & Jaakko O. Nieminen & Melanie Boly & Mario Rosanova & Silvia Casarotto & Olivier Bodart & Jitka Annen & Aurore Thibaut & Rajanikant Panda &, 2022. "Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
- Stefano Bianchini & Giacomo Damioli & Claudia Ghisetti, 2023. "The environmental effects of the “twin” green and digital transition in European regions," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 84(4), pages 877-918, April.
- Md Tauhidul Islam & Zixia Zhou & Hongyi Ren & Masoud Badiei Khuzani & Daniel Kapp & James Zou & Lu Tian & Joseph C. Liao & Lei Xing, 2023. "Revealing hidden patterns in deep neural network feature space continuum via manifold learning," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
- Wang, Zhengxin & Peng, Xinggan & Xia, Ao & Shah, Akeel A. & Yan, Huchao & Huang, Yun & Zhu, Xianqing & Zhu, Xun & Liao, Qiang, 2023. "Comparison of machine learning methods for predicting the methane production from anaerobic digestion of lignocellulosic biomass," Energy, Elsevier, vol. 263(PD).
- Shanshan Wang & Cheng Li & Rongpin Wang & Zaiyi Liu & Meiyun Wang & Hongna Tan & Yaping Wu & Xinfeng Liu & Hui Sun & Rui Yang & Xin Liu & Jie Chen & Huihui Zhou & Ismail Ayed & Hairong Zheng, 2021. "Annotation-efficient deep learning for automatic medical image segmentation," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
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Keywords
Computational biology and bioinformatics; Computer science;Statistics
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