ArticleFrontiers in bioengineering and biotechnology2021
One-Time Optimization of Advanced T Cell Culture Media Using a Machine Learning Pipeline.
Article in Frontiers in bioengineering and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 32 citations in OpenAlex.
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- Towards Cost-Effective and Sustainable Media Formulations for Terrestrial and Aquatic Cellular Agriculture.Foods (Basel, Switzerland) · 2026Review
- Are we there yet? The road to faster and more efficient CAR T cell manufacturing.Journal of biological engineering · 2026Review
- Nanoparticles-enhanced CAR-T cell therapy: current advances and future directions.Biomarker research · 2026Review
- Review
- Smart CAR-T Nanosymbionts: archetypes and proto-models.Frontiers in immunology · 2025Review
- Explainable AI for CHO cell culture media optimization and prediction of critical quality attribute.Applied microbiology and biotechnology · 2024Article
- Artificial intelligence and machine learning applications for cultured meat.Frontiers in artificial intelligence · 2024Review
- 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
- Natural killer cells in clinical development as non-engineered, engineered, and combination therapies.Journal of hematology & oncology · 2022Review
- Review
- 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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The growing application of cell and gene therapies in humans leads to a need for cell type-optimized culture media. Design of Experiments (DoE) is a successful and well known tool for the development and optimization of cell culture media for bioprocessing. When optimizing culture media for primary cells used in cell and gene therapy, traditional DoE approaches that depend on interpretable models will not always provide reliable predictions due to high donor variability. Here we present the implementation of a machine learning pipeline into the DoE-based design of cell culture media to optimize T cell cultures in one experimental step (one-time optimization). We applied a definitive screening design from the DoE toolbox to screen 12 major media components, resulting in 25 (2
Indexed as
Identifiers
What OpenQuestion holds
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.