ArticleScientific reports2017
Simplifying multidimensional fermentation dataset analysis and visualization: One step closer to capturing high-quality mutant strains.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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1 citing paper in PubMed.
- A Series of Efficient Umbrella Modeling Strategies to Track Irradiation-Mutation Strains Improving Butyric Acid Production From the Pre-development Earlier Stage Point of View.Frontiers in bioengineering and biotechnology · 2021Article
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3 authors.
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Abstract
In this study, we analyzed mutants of Clostridium acetobutylicum, an organism used in a broad range of industrial processes related to biofuel production, to facilitate future studies of bioreactor and bioprocess design and scale-up, which are very important research projects for industrial microbiology applications. To accomplish this, we generated 329 mutant strains and applied principal component analysis (PCA) to fermentation data gathered from these strains to identify a core set of independent features for comparison. By doing so, we were able to explain the differences in the mutant strains' fermentation expression states and simplify the analysis and visualization of the multidimensional datasets related to the strains. Our study has produced a high-efficiency PCA application based on a data analytics tool that is designed to visualize screening results and to support several hundred sets of data on fermentation interactions to assist researchers in more precisely screening and capturing high-quality mutant strains. More importantly, although this study focused on the use of PCA in microbial fermentation engineering, its results are broadly applicable.
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