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Author Correction: VAMPnets for deep learning of molecular kinetics

Author

Listed:
  • Andreas Mardt

    (Freie Universität Berlin)

  • Luca Pasquali

    (Freie Universität Berlin)

  • Hao Wu

    (Freie Universität Berlin)

  • Frank Noé

    (Freie Universität Berlin)

Abstract

In the original version of this Article, financial support was not fully acknowledged. The PDF and HTML versions of the Article have now been corrected to include funding from the Deutsche Forschungsgemeinschaft Grant SFB958/A04.

Suggested Citation

  • Andreas Mardt & Luca Pasquali & Hao Wu & Frank Noé, 2018. "Author Correction: VAMPnets for deep learning of molecular kinetics," Nature Communications, Nature, vol. 9(1), pages 1-1, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-06999-0
    DOI: 10.1038/s41467-018-06999-0
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    Cited by:

    1. Corneel Casert & Isaac Tamblyn & Stephen Whitelam, 2024. "Learning stochastic dynamics and predicting emergent behavior using transformers," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    2. Benjamin D Lee & Anthony Gitter & Casey S Greene & Sebastian Raschka & Finlay Maguire & Alexander J Titus & Michael D Kessler & Alexandra J Lee & Marc G Chevrette & Paul Allen Stewart & Thiago Britto-, 2022. "Ten quick tips for deep learning in biology," PLOS Computational Biology, Public Library of Science, vol. 18(3), pages 1-20, March.
    3. Yuxuan Zhuang & Rebecca J. Howard & Erik Lindahl, 2024. "Symmetry-adapted Markov state models of closing, opening, and desensitizing in α 7 nicotinic acetylcholine receptors," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    4. Shams Mehdi & Pratyush Tiwary, 2024. "Thermodynamics-inspired explanations of artificial intelligence," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    5. Joshua S. North & Christopher K. Wikle & Erin M. Schliep, 2023. "A Review of Data‐Driven Discovery for Dynamic Systems," International Statistical Review, International Statistical Institute, vol. 91(3), pages 464-492, December.
    6. Konstantin Avchaciov & Marina P. Antoch & Ekaterina L. Andrianova & Andrei E. Tarkhov & Leonid I. Menshikov & Olga Burmistrova & Andrei V. Gudkov & Peter O. Fedichev, 2022. "Unsupervised learning of aging principles from longitudinal data," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    7. Giacomo Janson & Gilberto Valdes-Garcia & Lim Heo & Michael Feig, 2023. "Direct generation of protein conformational ensembles via machine learning," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

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