ArticleMicroorganisms2020
Clustering of Bacterial Growth Dynamics in Response to Growth Media by Dynamic Time Warping.
Article in Microorganisms, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- Article
- Time Series Data Mining for Linking the Shape of Bacterial Growth Curves to Biological Functions.Computational and structural biotechnology journal · 2026Article
- Data-driven analysis reveals distinct genomic and environmental contributions to bacterial growth curves.Scientific reports · 2025Article
- Construction of primary chassis cells with efficient protein expression in Thermus thermophilus.Microbial cell factories · 2025Article
- Article
- Static Magnetic Field Inhibits Growth ofCells · 2022Article
- Global coordination of the mutation and growth rates across the genetic and nutritional variety inFrontiers in microbiology · 2022Article
- Correlation between the spatial distribution and colony size was common for monogenetic bacteria in laboratory conditions.BMC microbiology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Bacterial growth curves, representing population dynamics, are still poorly understood. The growth curves are commonly analyzed by model-based theoretical fitting, which is limited to typical S-shape fittings and does not elucidate the dynamics in their entirety. Thus, whether a certain growth condition results in any particular pattern of growth curve remains unclear. To address this question, up-to-date data mining techniques were applied to bacterial growth analysis for the first time. Dynamic time warping (DTW) and derivative DTW (DDTW) were used to compare the similarity among 1015 growth curves of 28
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Registered trials
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.