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

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 exper

SUBMITTER: Shen Z 

PROVIDER: S-EPMC8218935 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature