Evidence map›Paper›PMID 33444979›Full record

ArticleJournal of molecular graphics & modelling2021

Improving the speed of volumetric density map generation via cubic spline interpolation.

Daniel R Roe, Bernard R Brooks

Open access · greenAbstract read
In one paragraph

Article in Journal of molecular graphics & modelling, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 34% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Prediction Model of Hot Metal Silicon Content Based on Improved GA-BPNN.Computational intelligence and neuroscience · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Daniel R RoeLaboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA. Electronic address: daniel.roe@nih.gov.
Bernard R BrooksLaboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
National Institutes of Health · US

Funding

Development Of Advanced Computer Hardware And SoftwareZIHHL001052 · NHLBI · NATIONAL HEART, LUNG, AND BLOOD INSTITUTE · PI BROOKS, BERNARD R · 2009 to 2025
$12.2M
Theoretical Methods to Study Biological MacromoleculesZ01HL001051 · NHLBI · NATIONAL HEART, LUNG, AND BLOOD INSTITUTE · PI BROOKS, BERNARD R · 1998 to 2008
$1.4M
Intramural NIH HHS Z01 HL001051
6 · The paper itself

Abstract

Visualizing data generated from molecular dynamics simulations can be difficult, particularly when there can be thousands to millions of trajectory frames. The creation of a 3D grid of atomic density (i.e. a volumetric map) is one way to easily view the long-time average behavior of a system. One way to generate volumetric maps is by approximating each atom with a Gaussian function centered on that atom and spread over neighboring grid cells. However the calculation of the Gaussian function requires evaluation of the exponential function, which is computationally costly. Here we report on speeding up the calculation of volumetric maps from molecular dynamics trajectory data by replacing the expensive exponential function evaluation with an approximation using interpolating cubic splines. We also discuss the errors involved in this approximation, and recommend settings for volumetric map creation based on this.

Indexed as

Molecular Dynamics SimulationGrid densityMolecular dynamicsTrajectory analysisVolumetric maps

Identifiers

PMID33444979
PMCPMC7940575
OpenAlexW3118729079

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.