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.

中文翻译：

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使用机器学习破译二氧化钛纳米管上细胞增殖的争议结果

随着生物医学科学的快速发展，关于生物反应和材料特性之间关系的矛盾结果不断出现，给解释难以理解的界面过程增加了挑战。在本文中，我们使用二氧化钛纳米管 (TNT) 上的细胞增殖作为案例研究，并应用机器学习方法来解读文献中相互矛盾的结果。梯度提升决策树模型表明，在大多数出版物中，与其他可获得的实验特征相比，细胞密度对细胞增殖的影响更大。连同其他基本特征的变化，各种 TNT 上细胞增殖趋势的争论是可以理解的。通过使用穷举网格搜索策略遍历所有组合实验特征和相应的预测，我们发现调整细胞密度和灭菌方法可以同时在各种 TNT 直径上诱导相反的细胞增殖趋势，这通过实验进一步验证。该案例研究表明，机器学习是破译生物医学研究中有争议结果的新兴工具，为探索生物材料的结构-性能关系开辟了一条途径。