Approximating dynamic proximity with a hybrid geometry energy-based kernel for diffusion maps-Reference-Cited by-同舟云学术

Approximating dynamic proximity with a hybrid geometry energy-based kernel for diffusion maps

Published:2019-09-14 Issue:10 Volume:151 Page:105101
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Tan Qingzhe¹,Duan Mojie¹²^ORCID,Li Minghai¹,Han Li³,Huo Shuanghong¹^ORCID

Affiliation:

1. Gustaf H. Carlson School of Chemistry and Biochemistry, Clark University, 950 Main Street, Worcester, Massachusetts 01610, USA

2. Key Laboratory of Magnetic Resonance in Biological Systems, National Center for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China

3. Department of Math and Computer Science, Clark University, Worcester, Massachusetts 01610, USA

Funder

National Institutes of Health

National Natural Science Foundation of China

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://aip.scitation.org/doi/pdf/10.1063/1.5100968

Reference64 articles.

1. Essential dynamics of proteins

2. Large-amplitude nonlinear motions in proteins

3. Diffusion maps

4. B. Nadler, S. Lafon, R. R. Coifman, and I. G. Kevrekidis, in Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference, edited by Y. Weiss, B. Schölkopf, and J. Platt (MIT Press, Cambridge, MA, USA, 2005), Vol. 18, p. 955.

5. Diffusion Maps, Reduction Coordinates, and Low Dimensional Representation of Stochastic Systems

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