ArticleComputational and structural biotechnology journal2022
PERISCOPE-Opt: Machine learning-based prediction of optimal fermentation conditions and yields of recombinant periplasmic protein expressed in
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 16 citations in OpenAlex.
- Engineered Microbial Strains for Co-fermentation of C5 and C6 Sugars to Produce Cellulosic Ethanol.Advances in biochemical engineering/biotechnology · 2026Review
- Machine learning-based prediction of volatile compounds profiles in Saccharomyces cerevisiae fermentation simulating canned meat.NPJ science of food · 2025Article
- Towards AI-designed genomes using a variational autoencoder.Proceedings. Biological sciences · 2024Article
- Immunogenic potential and neutralizing ability of a heterologous version of the most abundant three-finger toxin from the coral snakeThe journal of venomous animals and toxins including tropical diseases · 2024Article
- Machine learning-assisted medium optimization revealed the discriminated strategies for improved production of the foreign and native metabolites.Computational and structural biotechnology journal · 2023Article
- Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals.Frontiers in plant science · 2023Review
- Article
- Recent advances in culture medium design for enhanced production of monoclonal antibodies in CHO cells: A comparative study of machine learning and systems biology approaches.Biotechnology advancesReview
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optimization of the fermentation process for recombinant protein production (RPP) is often resource-intensive. Machine learning (ML) approaches are helpful in minimizing the experimentations and find vast applications in RPP. However, these ML-based tools primarily focus on features with respect to amino-acid-sequence, ruling out the influence of fermentation process conditions. The present study combines the features derived from fermentation process conditions with that from amino acid-sequence to construct an ML-based model that predicts the maximal protein yields and the corresponding fermentation conditions for the expression of target recombinant protein in the
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Registered trials
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