ArticleRegenerative biomaterials2021
Deciphering controversial results of cell proliferation on TiO
Article in Regenerative biomaterials, 2021. 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.
- Single-cell transcriptome-guided biomimetic magnetothermal hydrogel microspheres for multimodal eradication of residual glioblastoma.Bioactive materials · 2026Article
- Machine learning-based optimization of cytotoxicity testing for assessing Zn-based biodegradable metals.Materials today. Bio · 2025Article
- Integrating graph convolutional networks with large language models for structured biomedical material knowledge representation.Regenerative biomaterials · 2025Article
- Dimensional effects of surface morphology and trapped air on mammalian cell adhesion to special wetting surfaces.Regenerative biomaterials · 2025Article
- Regression models for the prediction of the influence of magnesium ions on primary endothelial cell (HUVEC) proliferation and migration.Computational and structural biotechnology journal · 2025Article
- Mapping Biomaterial Complexity by Machine Learning.Tissue engineering. Part A · 2024Review
- Integrating machine learning for the optimization of polyacrylamide/alginate hydrogel.Regenerative biomaterials · 2024Article
- Prediction of the Ibuprofen Loading Capacity of MOFs by Machine Learning.Bioengineering (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rapid development of biomedical sciences, contradictory results on the relationships between biological responses and material properties emerge continuously, adding to the challenge of interpreting the incomprehensible interfacial process. In the present paper, we use cell proliferation on titanium dioxide nanotubes (TNTs) as a case study and apply machine learning methodologies to decipher contradictory results in the literature. The gradient boosting decision tree model demonstrates that cell density has a higher impact on cell proliferation than other obtainable experimental features in most publications. Together with the variation of other essential features, the controversy of cell proliferation trends on various TNTs is understandable. By traversing all combinational experimental features and the corresponding forecast using an exhausted grid search strategy, we find that adjusting cell density and sterilization methods can simultaneously induce opposite cell proliferation trends on various TNTs diameter, which is further validated by experiments. This case study reveals that machine learning is a burgeoning tool in deciphering controversial results in biomedical researches, opening up an avenue to explore the structure-property relationships of biomaterials.
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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.