ReviewFrontiers in cellular neuroscience2025
The paradigm shift in neural stem cells basic research driven by artificial intelligence related technologies.
Review in Frontiers in cellular neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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3 authors.
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Abstract
Neural stem cells (NSCs) hold significant potential in neural regenerative medicine, yet research faces multiple challenges such as cellular heterogeneity, unclear microenvironment interactions, and low clinical translation efficiency. In recent years, the rapid development of artificial intelligence (AI) technologies has provided new ideas and tools to address these issues. This paper reviews the current applications of AI in fundamental NSCs research, including intelligent identification, deep learning-driven subtype analysis, spatial microenvironment deconstruction, and dynamic analysis of neural differentiation. Additionally, we discuss several key AI technologies not yet applied to NSCs research, such as generative adversarial networks, graph neural networks, and self-supervised learning, as well as their potential applications in cell classification, interaction network analysis, and morphological feature extraction. Although AI technologies show great promise in NSCs research, challenges remain regarding data quality, model robustness, and interpretability. Therefore, future research should focus on establishing high-quality standardized multimodal data platforms and integrating biological knowledge to enhance model interpretability, thereby deepening the understanding of NSCs biological characteristics and differentiation mechanisms and advancing personalized therapies.
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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.