ArticleNature communications2021
Deep learning-based predictive identification of neural stem cell differentiation.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers.
What it found
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Who cites it
103 citing papers in PubMed.
- AI for bioactive materials: From material design to biological applications.Bioactive materials · 2026Review
- From pixels to patterns: the AI revolution in stem cell-derived models.Nature methods · 2026Review
- Stimulus-responsive molecular imaging propels stem cell research: from passive localization to active event recognition.European journal of nuclear medicine and molecular imaging · 2026Article
- Activin A enhances neurofunctional recovery following traumatic spinal cord injury by inhibiting autophagy.Neural regeneration research · 2026Article
- Revolutionizing Stem Cell Sorting with Machine Learning: A Review of Trends, Tools, and Future Directions.Iranian journal of medical sciences · 2026Review
- BrAIn: A comprehensive artificial intelligence-based morphology analysis system for brain organoids and neuroscience.Bioengineering & translational medicine · 2026Article
- Deep learning for predicting stem cell efficiency for use in beta cell differentiation.Scientific reports · 2026Article
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- Development and validation of deep learning for predicting the growth of ovarian cancer organoids.Chinese medical journal · 2026Article
- Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation.Computational and structural biotechnology journal · 2026Review
- Direct neuronal reprogramming for neurological diseases: applications, translational Challenges, and future directions.Frontiers in neuroscience · 2026Review
- Theragenerative Nanomaterials: Integrating Therapy, Regeneration, and Diagnosis-Navigating the Shared Pathways Between Tissue Repair and Malignancy.International journal of nanomedicine · 2026Review
- Automated bone marrow cell classification using ensemble learning: performance, generalization, and clinical interpretability.Frontiers in medicine · 2026Article
- CG-RecNet: a gated and attention-fused deep learning framework for label-free classification of neural stem cell differentiation via imaging flow cytometry.Frontiers in cell and developmental biology · 2026Article
- Astragaloside-Brucea Javanica Oil Nanoemulsion Regulates Glycolysis in Oral Squamous Cell Carcinoma Through AURKA-Mediated PI3K/AKT/HIF-1α Pathway.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Biomaterial-Based Emergency Intervention for Secondary Spinal Cord Injury.Small science · 2025Review
- A New Insight in Cellular and Molecular Signaling Regulation for Neural Differentiation Program.Molecular neurobiology · 2025Review
- Selenized neural stem cell-derived exosomes: A neotype therapeutic agent for traumatic injuries of the central nervous system.Cell reports. Medicine · 2025Article
- ZnO Nanoparticle-Based Electrochemical Immunosensor for One-Step Quantification of Cortisol in Saliva.ACS omega · 2025Article
- Generation of Neural Organoids and Their Application in Disease Modeling and Regenerative Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
43 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The differentiation of neural stem cells (NSCs) into neurons is proposed to be critical in devising potential cell-based therapeutic strategies for central nervous system (CNS) diseases, however, the determination and prediction of differentiation is complex and not yet clearly established, especially at the early stage. We hypothesize that deep learning could extract minutiae from large-scale datasets, and present a deep neural network model for predictable reliable identification of NSCs fate. Remarkably, using only bright field images without artificial labelling, our model is surprisingly effective at identifying the differentiated cell types, even as early as 1 day of culture. Moreover, our approach showcases superior precision and robustness in designed independent test scenarios involving various inducers, including neurotrophins, hormones, small molecule compounds and even nanoparticles, suggesting excellent generalizability and applicability. We anticipate that our accurate and robust deep learning-based platform for NSCs differentiation identification will accelerate the progress of NSCs applications.
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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.