Linear Binary Classifier to Predict Bacterial Biofilm Formation on Polyacrylates.
Linear Binary Classifier to Predict Bacterial Biofilm Formation on Polyacrylates.
复制标题
线性二元分类器预测聚丙烯酸酯上细菌生物膜的形成。
DOI：
10.1021/acsami.2c23182
复制
发表时间：
2023-03-07
影响因子：
9.5
通讯作者：
Williams, Philip M.
中科院分区：
文献类型：
--
作者：
Contreas, Leonardo;Hook, Andrew L.;Winkler, David A.;Figueredo, Grazziela;Williams, Paul;Laughton, Charles A.;Alexander, Morgan R.;Williams, Philip M.
关键词：
由于抗菌素耐药性的上升，细菌感染问题日益严重。因此，合理设计天然抵抗生物膜形成的材料是预防医疗器械相关感染的重要策略。机器学习（ML）是一种从广泛领域的复杂数据中发现有用模式的强大方法。最近的报告显示，ML如何揭示细菌粘附与聚丙烯酸酯文库的物理化学性质之间的密切关系。这些研究采用鲁棒和预测的非线性回归方法，比线性模型具有更好的定量预测能力。然而，由于非线性模型的特征重要性是局部的而不是全局的，这些模型很难解释，并且对材料-细菌相互作用的分子细节提供了有限的见解。在这里，我们表明使用可解释的质谱分子离子和化学信息学描述符以及三种常见医院病原体附着于聚丙烯酸酯库的线性二元分类模型可以为设计更有效的病原体抗性涂层提供更好的指导。分析了每个模型的相关特征，并将其与易于解释的化学信息学描述符相关联，从而得出一组规则，这些规则赋予模型特征有形的含义，阐明了结构和功能之间的关系。结果表明，利用化学信息学描述符可以对铜绿假单胞菌和金黄色葡萄球菌的附着进行稳健预测，表明所获得的模型可以预测聚丙烯酸酯的附着响应，为今后合成和测试抗附着材料提供依据。
Bacterial infections are increasingly problematic due to the rise of antimicrobial resistance. Consequently, the rational design of materials naturally resistant to biofilm formation is an important strategy for preventing medical device-associated infections. Machine learning (ML) is a powerful method to find useful patterns in complex data from a wide range of fields. Recent reports showed how ML can reveal strong relationships between bacterial adhesion and the physicochemical properties of polyacrylate libraries. These studies used robust and predictive nonlinear regression methods that had better quantitative prediction power than linear models. However, as nonlinear models’ feature importance is a local rather than global property, these models were hard to interpret and provided limited insight into the molecular details of material–bacteria interactions. Here, we show that the use of interpretable mass spectral molecular ions and chemoinformatic descriptors and a linear binary classification model of attachment of three common nosocomial pathogens to a library of polyacrylates can provide improved guidance for the design of more effective pathogen-resistant coatings. Relevant features from each model were analyzed and correlated with easily interpretable chemoinformatic descriptors to derive a small set of rules that give model features tangible meaning that elucidate relationships between the structure and function. The results show that the attachment of Pseudomonas aeruginosa and Staphylococcus aureus can be robustly predicted by chemoinformatic descriptors, suggesting that the obtained models can predict the attachment response to polyacrylates to identify anti-attachment materials to synthesize and test in the future.
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影响因子：
9.5
作者：
Mikulskis P;Hook A;Dundas AA;Irvine D;Sanni O;Anderson D;Langer R;Alexander MR;Williams P;Winkler DA
通讯作者：
Winkler DA
影响因子：
21.8
作者：
通讯作者：
--
影响因子：
46.9
作者：
通讯作者：
--
影响因子：
2.9
作者：
Guest JF;Fuller GW;Vowden P
通讯作者：
Vowden P
DOI：
10.1016/0005-2795(75)90109-9
发表时间：
1975-01-01
期刊：
BIOCHIMICA ET BIOPHYSICA ACTA
影响因子：
--
作者：
MATTHEWS, BW
通讯作者：
MATTHEWS, BW