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

ABSTRACT: The diffusion map is a dimensionality reduction method. The reduction coordinates are associated with the leading eigenfunctions of the backward Fokker-Planck operator, providing a dynamic meaning for these coordinates. One of the key factors that affect the accuracy of diffusion map embedding is the dynamic measure implemented in the Gaussian kernel. A common practice in diffusion map study of molecular systems is to approximate dynamic proximity with RMSD (root-mean-square deviation). In this paper, we present a hybrid geometry-energy based kernel. Since high energy-barriers may exist between geometrically similar conformations, taking both RMSD and energy difference into account in the kernel can better describe conformational transitions between neighboring conformations and lead to ac

SUBMITTER: Tan Q 

PROVIDER: S-EPMC6733700 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature