2023
sMolBoxes: Dataflow Model for Molecular Dynamics Exploration
ULBRICH, Pavol; Manuela WALDNER; Katarína FURMANOVÁ; Sérgio Manuel MARQUES; David BEDNÁŘ et al.Základní údaje
Originální název
sMolBoxes: Dataflow Model for Molecular Dynamics Exploration
Autoři
ULBRICH, Pavol; Manuela WALDNER; Katarína FURMANOVÁ ORCID; Sérgio Manuel MARQUES; David BEDNÁŘ; Barbora KOZLÍKOVÁ ORCID a Jan BYŠKA ORCID
Vydání
IEEE Transactions on Visualization and Computer Graphics, United States, IEEE Computer Society, 2023, 1077-2626
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Obor
10201 Computer sciences, information science, bioinformatics
Stát vydavatele
Spojené státy
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Impakt faktor
Impact factor: 4.700
Označené pro přenos do RIV
Ano
Kód RIV
RIV/00216224:14330/23:00130033
Organizační jednotka
Fakulta informatiky
UT WoS
EID Scopus
Klíčová slova anglicky
Molecular dynamics;structure;node-based visualization;progressive analytics
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 25. 7. 2024 07:58, Mgr. Marie Novosadová Šípková, DiS.
Anotace
V originále
We present sMolBoxes, a dataflow representation for the exploration and analysis of long molecular dynamics (MD) simulations. When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable fluid switching between multiple properties, which facilitates hypothesis generation. Each sMolBox provides quick insight to an observed property or function, available in more detail in the bigBox View. The case studies illustrate that even with relatively few sMolBoxes, it is possible to express complex analytical tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.
Návaznosti
| GJ20-15915Y, projekt VaV |
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| LM2018131, projekt VaV |
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| LM2018140, projekt VaV |
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| MUNI/A/1230/2021, interní kód MU |
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| MUNI/A/1339/2022, interní kód MU |
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