Awesome-machine-or-deep-learning-related-papers-on-Cell-Nature-Science-series-of-journals

Artificial intelligence is a strategic technology that leads a new round of technological revolution and industrial transformation. Machine learning technology represented by deep learning is the core of artificial intelligence, which has achieved great success in many fields. There is no doubt that Cell-Nature-Science-series-of-journals are recognized as the top three journals in the world. To a great extent, they guide the future development trend. In view of this, this project focuses on machine learning or deep learning related papers on Cell-Nature-Science-series-of-journals, lists relevant must-read papers and keeps track of progress. We look forward to promoting this direction and providing some help to researchers in this direction.

Contributed by Allen Bluce. If there are some questions, welcome to send E-mail ([email protected], [email protected])

This is just the tip of the iceberg!!! So many papers related to Ml/DL/NN have published on Cell-Nature-Science-series-of-journals such as Nature communications, Science advances.

Review Papers

1. Crystal symmetry determination in electron diffraction using machine learning. Kevin Kaufmann, et al. Science, 2020. paper

2. Next-Generation Machine Learning for Biological Networks. Diogo M. Camacho, et al. Cell, 2019. paper

3. Deep learning for cellular image analysis. Erick Moen, et al. Nature Methods, 2019. paper

4. Quantum machine learning. Jacob Biamonte, et al. Nature, 2017. paper

5. Deep learning. Yann LeCun, Yoshua Bengio & Geoffrey Hinton. Nature, 2015. paper

6. Reinforcement learning improves behaviour from evaluative feedback. Michael L. Littman. Nature, 2015. paper

7. Probabilistic machine learning and artificial intelligence. Zoubin Ghahramani. Nature, 2015. paper

8. Neural networks and perceptual learning. Misha Tsodyks & Charles Gilbert Nature, 2004. paper

9. Holography in artificial neural networks. Demetri Psaltis, et al. Nature, 1990. paper

10. Machine learning for data-driven discovery in solid Earth geoscience. Karianne J. Bergen, et al. Science, 2019. paper

11. Inverse molecular design using machine learning: Generative models for matter engineering. Benjamin Sanchez-Lengeling, et al. Science, 2018. paper

12. Machine learning: Trends, perspectives, and prospects. M. I. Jordan, T. M. Mitchell Science, 2015. paper

Research Papers on Cell Journal

1. How Machine Learning Will Transform Biomedicine. Goecks, Jeremy, et al. Cell, 2020. paper

2. A Deep Learning Approach to Antibiotic Discovery. Stokes, JM, et al. Cell, 2020. paper

3. Deep Learning Reveals Cancer Metastasis and Therapeutic Antibody Targeting in the Entire Body. Pan, CC, et al. Cell, 2019. paper

4. A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation. Nicholas Bogard, et al. Cell, 2019. paper

5. Predicting Splicing from Primary Sequence with Deep Learning. Kishore Jaganathan, et al. Cell, 2019. paper

6. A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action. Jason H. Yang, et al. Cell, 2019. paper

7. Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences. Carlos R. Ponce, et al. Cell, 2019. paper

8. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Daniel S. Kermany, et al. Cell, 2018. paper

Research Papers on Nature Journal

1. Fully hardware-implemented memristor convolutional neural network. Peng Yao, et al. Nature, 2020. paper

2. A distributional code for value in dopamine-based reinforcement learning. Dabney, Will, et al. Nature, 2020. paper

3. Closed-loop optimization of fast-charging protocols for batteries with machine learning. Peter M. Attia, et al. Nature, 2020. paper

4. Improved protein structure prediction using potentials from deep learning. Andrew W. Senior, et al. Nature, 2020. paper

5. Grandmaster level in StarCraft II using multi-agent reinforcement learning. Oriol Vinyals, et al. Nature, 2019. paper

6. One neuron versus deep learning in aftershock prediction. Arnaud Mignan, et al. Nature, 2019. paper

7. Unsupervised word embeddings capture latent knowledge from materials science literature. Tshitoyan V, et al. Nature, 2019. paper

8. Deep learning for multi-year ENSO forecasts. Yoo-Geun Ham, et al. Nature, 2019. paper

9. Learning the signatures of the human grasp using a scalable tactile glove. Subramanian Sundaram, et al. Nature, 2019. paper

10. Supervised learning with quantum-enhanced feature spaces. Vojtěch Havlíček, et al. Nature, 2019. paper

11. Deep learning and process understanding for data-driven Earth system science. Markus Reichstein, et al. Nature, 2019. paper

12. Deep learning of aftershock patterns following large earthquakes. Phoebe M. R. DeVries, et al. Nature, 2019. paper

13. Machine learning at the energy and intensity frontiers of particle physics. Alexander Radovic, et al. Nature, 2018. paper

14. Machine learning for molecular and materials science. Keith T. Butler, et al. Nature, 2018. paper

15. Vector-based navigation using grid-like representations in artificial agents. Andrea Banino, et al. Nature, 2018. paper

16. Planning chemical syntheses with deep neural networks and symbolic AI. Marwin H. S. Segler, et al. Nature, 2018. paper

17. Equivalent-accuracy accelerated neural-network training using analogue memory. Stefano Ambrogio, et al. Nature, 2018. paper

18. Image reconstruction by domain-transform manifold learning. Bo Zhu, et al. Nature, 2018. paper

19. Fast automated analysis of strong gravitational lenses with convolutional neural networks. Yashar D. Hezaveh, et al. Nature, 2017. paper

20. Dermatologist-level classification of skin cancer with deep neural networks. Andre Esteva, et al. Nature, 2017. paper

21. Hybrid computing using a neural network with dynamic external memory. Alex Graves, et al. Nature, 2016. paper

22. Mastering the game of Go with deep neural networks and tree search. David Silver, et al. Nature, 2016. paper

23. Human-level control through deep reinforcement learning. Volodymyr Mnih, et al. Nature, 2015. paper

24. Neural constraints on learning. Patrick T. Sadtler, et al. Nature, 2014. paper

25. Self-organizing neural network that discovers surfaces in random-dot stereograms. Suzanna Becker & Geoffrey E. Hinton. Nature, 1992. paper

26. Function of identified interneurons in the leech elucidated using neural networks trained by back-propagation. Shawn R. Lockery, et al. Nature, 1989. paper

Research Papers on Science Journal

1. Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning. Frank Noé, et al. Science, 2019. paper

2. Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning. Andrew F. Zahrt, et al. Science, 2019. paper

3. Human-level performance in 3D multiplayer games with population-based reinforcement learning. Jaderberg, Max, et al. Science, 2019. paper

4. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. * Silver, David, et al.* Science, 2018. paper

5. Combining satellite imagery and machine learning to predict poverty. Neal Jean, et al. Science, 2016. paper