ArticleBioMed research international2014
Serum-free medium optimization based on trial design and support vector regression.
Article in BioMed research international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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.
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Who cites it
6 citing papers in PubMed.
- Development of Cell Culture Media for Cultivated Meat Production.Advances in biochemical engineering/biotechnology · 2026Review
- Employing active learning in the optimization of culture medium for mammalian cells.NPJ systems biology and applications · 2023Article
- A review of algorithmic approaches for cell culture media optimization.Frontiers in bioengineering and biotechnology · 2023Review
- Serum-Free Medium for Recombinant Protein Expression in Chinese Hamster Ovary Cells.Frontiers in bioengineering and biotechnology · 2021Review
- A statistical approach to improve compound screening in cell culture media.Engineering in life sciences · 2019Article
- Optimization of Key Factors in Serum Free Medium for Production of Human Recombinant GM-CSF Using Response Surface Methodology.Iranian journal of pharmaceutical research : IJPR · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Plackett-Burman design and support vector machine (SVM) were reported to be used on many fields such as some feature selections, protein structure prediction, or forecasting of other situations. Here, with suspension adapted Chinese hamster ovary (CHO) cells as the object of study, a serum-free medium for the culture of CHO cells in suspension was optimized by this method. Support vector machine based on genetic algorithm was used to predict the growth rate of CHO and prove the results from the trial designs. Experimental results indicated that ZnSO4, transferrin, and bovine serum albumin (BSA) were important ones. The same conclusion was arrived at when the support vector regression model analyzed the experimental results. With the methods mentioned, the influence of 7 medium supplements on the growth of CHO cells in suspension was evaluated efficiently.
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Registered trials
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