AI Medical Compendium Journal:
Journal of bioscience and bioengineering

Showing 1 to 9 of 9 articles

Deep-learning-assisted medium optimization improves hyaluronic acid production by Streptococcus zooepidemicus.

Journal of bioscience and bioengineering Apr 3, 2025
To improve the efficiency of hyaluronic acid production by Streptococcus zooepidemicus, the growth medium was optimized with a pipeline involving a deep learning (DL) algorithm. To train the DL model, the initial training dataset (OA01-18) was design...
Deep Learning Culture Media Algorithms Batch Cell Culture Techniques Streptococcus equi Hyaluronic Acid Bioreactors
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Application of deep learning for evaluation of the growth rate of Daphnia magna.

Journal of bioscience and bioengineering Feb 17, 2025
For the safe use of chemicals widely used in human activities, it is crucial to assess their ecological impacts when released into the environment. Daphnia, a well-established environmental indicator species, is commonly used to evaluate the biologic...
Animals Deep Learning Daphnia magna Daphnia Image Processing, Computer-Assisted
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Importance of dataset design in developing robust U-Net models for label-free cell morphology evaluation.

Journal of bioscience and bioengineering Feb 10, 2025
Advances in regenerative medicine highlighted the need for label-free cell image analysis to replace conventional microscopic observation for non-invasive cell quality evaluation. Image-based evaluation provides an efficient, quantitative, and automa...
Humans Regenerative Medicine Cell Shape Datasets as Topic Animals Image Processing, Computer-Assisted Neural Networks, Computer
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High cell density cultivation of Corynebacterium glutamicum by deep learning-assisted medium design and the subsequent feeding strategy.

Journal of bioscience and bioengineering Mar 2, 2024
To improve the cell productivity of Corynebacterium glutamicum, its initial specific growth rate was improved by medium improvement using deep neural network (DNN)-assisted design with Bayesian optimization (BO) and a genetic algorithm (GA). To obtai...
Corynebacterium glutamicum Cell Count Glucose Magnesium Deep Learning Bayes Theorem
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Optimization of medium components for protein production by Escherichia coli with a high-throughput pipeline that uses a deep neural network.

Journal of bioscience and bioengineering Feb 1, 2024
To optimize rapidly the medium for green fluorescent protein expression by Escherichia coli with an introduced plasmid, pRSET/emGFP, a single-cycle optimization pipeline was applied. The pipeline included a deep neural network (DNN) and mathematical ...
Green Fluorescent Proteins Algorithms Neural Networks, Computer Bayes Theorem Escherichia coli
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Prediction of ethanol fermentation under stressed conditions using yeast morphological data.

Journal of bioscience and bioengineering Jan 13, 2023
A high sugar concentration is used as a starting condition in alcoholic fermentation by budding yeast, which shows changes in intracellular state and cell morphology under conditions of high-sugar stress. In this study, we developed artificial intell...
Ethanol Carbohydrates Glucose Artificial Intelligence Sugars Fermentation Saccharomyces cerevisiae
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High throughput optimization of medium composition for Escherichia coli protein expression using deep learning and Bayesian optimization.

Journal of bioscience and bioengineering Dec 29, 2022
To improve synthetic media for protein expression in Escherichia coli, a strategy using deep neural networks (DNN) and Bayesian optimization was performed in this study. To obtain training data for a deep learning algorithm, E. coli harvesting a plas...
Escherichia coli Bayes Theorem Deep Learning Green Fluorescent Proteins Escherichia coli Proteins
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Pseudo-nuclear staining of cells by deep learning improves the accuracy of automated cell counting in a label-free cellular population.

Journal of bioscience and bioengineering Oct 17, 2020
Deep learning has emerged as a breakthrough tool for the segmentation of images without supporting human experts. Here, we propose an automated approach that uses deep learning to generate pseudo-nuclear staining of cells from phase contrast images. ...
Cell Count Deep Learning Staining and Labeling Humans Automation Microscopy
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Fake metabolomics chromatogram generation for facilitating deep learning of peak-picking neural networks.

Journal of bioscience and bioengineering Oct 10, 2020
Finding peaks in chromatograms and determining their start and end points (peak picking) is a core task in chromatography based biotechnology. Construction of peak-picking neural networks by deep learning was, however, hampered from the preparation o...
Deep Learning Metabolomics Software Chromatography
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