Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385890231> ?p ?o ?g. }
Showing items 1 to 77 of
77
with 100 items per page.
- W4385890231 abstract "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 reasons. First, it comes with a rich structure to account for a wide range of geometries that can be modelled after an energy landscape. Second, many standard data analysis tools initially developed for data in Euclidean space can also be generalised to data on a Riemannian manifold. In the context of protein dynamics, a conceptual challenge comes from the lack of a suitable smooth manifold and the lack of guidelines for constructing a smooth Riemannian structure based on an energy landscape. In addition, computational feasibility in computing geodesics and related mappings poses a major challenge. This work considers these challenges. The first part of the paper develops a novel local approximation technique for computing geodesics and related mappings on Riemannian manifolds in a computationally feasible manner. The second part constructs a smooth manifold of point clouds modulo rigid body group actions and a Riemannian structure that is based on an energy landscape for protein conformations. The resulting Riemannian geometry is tested on several data analysis tasks relevant for protein dynamics data. It performs exceptionally well on coarse-grained molecular dynamics simulated data. In particular, the geodesics with given start- and end-points approximately recover corresponding molecular dynamics trajectories for proteins that undergo relatively ordered transitions with medium sized deformations. The Riemannian protein geometry also gives physically realistic summary statistics and retrieves the underlying dimension even for large-sized deformations within seconds on a laptop." @default.
- W4385890231 created "2023-08-17" @default.
- W4385890231 creator A5027503123 @default.
- W4385890231 creator A5029375363 @default.
- W4385890231 creator A5033880300 @default.
- W4385890231 creator A5039754146 @default.
- W4385890231 creator A5078374818 @default.
- W4385890231 date "2023-08-15" @default.
- W4385890231 modified "2023-09-27" @default.
- W4385890231 title "Riemannian geometry for efficient analysis of protein dynamics data" @default.
- W4385890231 doi "https://doi.org/10.48550/arxiv.2308.07818" @default.
- W4385890231 hasPublicationYear "2023" @default.
- W4385890231 type Work @default.
- W4385890231 citedByCount "0" @default.
- W4385890231 crossrefType "posted-content" @default.
- W4385890231 hasAuthorship W4385890231A5027503123 @default.
- W4385890231 hasAuthorship W4385890231A5029375363 @default.
- W4385890231 hasAuthorship W4385890231A5033880300 @default.
- W4385890231 hasAuthorship W4385890231A5039754146 @default.
- W4385890231 hasAuthorship W4385890231A5078374818 @default.
- W4385890231 hasBestOaLocation W43858902311 @default.
- W4385890231 hasConcept C109546454 @default.
- W4385890231 hasConcept C11413529 @default.
- W4385890231 hasConcept C114614502 @default.
- W4385890231 hasConcept C12520029 @default.
- W4385890231 hasConcept C127413603 @default.
- W4385890231 hasConcept C165818556 @default.
- W4385890231 hasConcept C166957645 @default.
- W4385890231 hasConcept C181104567 @default.
- W4385890231 hasConcept C184720557 @default.
- W4385890231 hasConcept C186450821 @default.
- W4385890231 hasConcept C195065555 @default.
- W4385890231 hasConcept C202444582 @default.
- W4385890231 hasConcept C205649164 @default.
- W4385890231 hasConcept C2524010 @default.
- W4385890231 hasConcept C2779343474 @default.
- W4385890231 hasConcept C2779593128 @default.
- W4385890231 hasConcept C33923547 @default.
- W4385890231 hasConcept C41008148 @default.
- W4385890231 hasConcept C529865628 @default.
- W4385890231 hasConcept C78519656 @default.
- W4385890231 hasConceptScore W4385890231C109546454 @default.
- W4385890231 hasConceptScore W4385890231C11413529 @default.
- W4385890231 hasConceptScore W4385890231C114614502 @default.
- W4385890231 hasConceptScore W4385890231C12520029 @default.
- W4385890231 hasConceptScore W4385890231C127413603 @default.
- W4385890231 hasConceptScore W4385890231C165818556 @default.
- W4385890231 hasConceptScore W4385890231C166957645 @default.
- W4385890231 hasConceptScore W4385890231C181104567 @default.
- W4385890231 hasConceptScore W4385890231C184720557 @default.
- W4385890231 hasConceptScore W4385890231C186450821 @default.
- W4385890231 hasConceptScore W4385890231C195065555 @default.
- W4385890231 hasConceptScore W4385890231C202444582 @default.
- W4385890231 hasConceptScore W4385890231C205649164 @default.
- W4385890231 hasConceptScore W4385890231C2524010 @default.
- W4385890231 hasConceptScore W4385890231C2779343474 @default.
- W4385890231 hasConceptScore W4385890231C2779593128 @default.
- W4385890231 hasConceptScore W4385890231C33923547 @default.
- W4385890231 hasConceptScore W4385890231C41008148 @default.
- W4385890231 hasConceptScore W4385890231C529865628 @default.
- W4385890231 hasConceptScore W4385890231C78519656 @default.
- W4385890231 hasLocation W43858902311 @default.
- W4385890231 hasOpenAccess W4385890231 @default.
- W4385890231 hasPrimaryLocation W43858902311 @default.
- W4385890231 hasRelatedWork W1975282624 @default.
- W4385890231 hasRelatedWork W2155379735 @default.
- W4385890231 hasRelatedWork W2164995684 @default.
- W4385890231 hasRelatedWork W2808583987 @default.
- W4385890231 hasRelatedWork W2899393262 @default.
- W4385890231 hasRelatedWork W2945976040 @default.
- W4385890231 hasRelatedWork W2946135896 @default.
- W4385890231 hasRelatedWork W2999284236 @default.
- W4385890231 hasRelatedWork W4289306376 @default.
- W4385890231 hasRelatedWork W4385965779 @default.
- W4385890231 isParatext "false" @default.
- W4385890231 isRetracted "false" @default.
- W4385890231 workType "article" @default.