Deciphering controversial results of cell proliferation on TiO2 nanotubes using machine learning

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.

Automated summary

Through machine learning methodologies, this study examined cell proliferation on titanium dioxide nanotubes (TNTs), revealing the significant impact of cell density and the contradictory results on cell proliferation trends across different experimental features. The results demonstrate that adjusting cell density and sterilization methods can induce opposite cell proliferation trends on various TNTs diameter, providing insights into the structure-property relationships of biomaterials. This case study showcases the growing potential of machine learning in deciphering controversial results in biomedical research.