Evidence map›Paper›PMID 30763265›Full record

ArticleJournal of integrative bioinformatics2019

Dynamic Load Balancing Strategy for Parallel Tumor Growth Simulations.

Alberto G Salguero, Antonio J Tomeu-Hardasmal, Manuel I Capel

Abstract read
In one paragraph

Article in Journal of integrative bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

3 authors.

Alberto G SalgueroUniversity of Cádiz, Computer Science, Puerto Real, Spain.ORCID https://orcid.org/0000-0001-9221-7351
Antonio J Tomeu-HardasmalUniversity of Cádiz, Computer Science, Puerto Real, Spain.ORCID https://orcid.org/0000-0003-4380-9785
Manuel I CapelUniversity of Granada, Granada, Spain.ORCID https://orcid.org/0000-0003-2449-4394

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we propose a parallel cellular automaton tumor growth model that includes load balancing of cells distribution among computational threads with the introduction of adjusting parameters. The obtained results show a fair reduction in execution time and improved speedup compared with the sequential tumor growth simulation program currently referenced in tumoral biology. The dynamic data structures of the model can be extended to address additional tumor growth characteristics such as angiogenesis and nutrient intake dependencies.

Indexed as

AlgorithmsCell ProliferationComputer SimulationModels, BiologicalComputational BiologyHumansNeoplasmsTumor Cells, CulturedCellular AutomatonHigh Performance ComputingMathematical OncologyParallel ProgrammingTumoral Growth Simulation

Identifiers

PMID30763265
PMCPMC6798858

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.