Riemannian geometry for efficient analysis of protein dynamics data

An increasingly common viewpoint is that protein dynamics data sets reside in a non-linear subspace of low conformational energy. Ideal data analysis tools for such data sets should therefore account for such non-linear geometry. The Riemannian geometry setting can be suitable for a variety of reaso...

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Veröffentlicht in:	arXiv.org 2023-10
Hauptverfasser:	Diepeveen, Willem, Esteve-Yagüe, Carlos, Lellmann, Jan, Öktem, Ozan, Schönlieb, Carola-Bibiane
Format:	Artikel
Sprache:	eng
Schlagworte:	Computation Data analysis Datasets Deformation Euclidean geometry Feasibility Geodesy Geometry Manifolds (mathematics) Molecular dynamics Proteins Riemann manifold Rigid structures Standard data Topological manifolds
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