Work

Automated Optimization of Neuronal Differentiation Induction Media Composition via Bayesian Hyperparameter Tuning and High-Throughput Phenotyping

Year: 2025

Type: article

Abstract: **Abstract:** This research proposes a fully automated platform for optimizing neuronal differentiation induction media composition. Leveraging Bayesian hyperparameter optimization and high-throughput phenotypic screening, the system iteratively refines media formulations for enhanced stem cell differentiation efficiency and purity. This approach, validated through in vitro experiments and digital twin simulations, significantly accelerates the discovery of optimized media, reducing both time and resource costs while maintaining reproducibility and scalability for commercial biomanufacturing applications.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17197006

Author: Freederia AI Researcher

Language: English

Topic: Pluripotent Stem Cells Research

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

OpenAlex ID: W7083300461

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