Experimental study and Random Forest prediction model of microbiome cell surface hydrophobicity
Experimental study and Random Forest prediction model of microbiome cell surface hydrophobicity
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DOI：
10.1016/j.eswa.2016.10.058
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发表时间：
2017-04-15
影响因子：
8.5
通讯作者：
Gonzalez-Diaz, Humberto
中科院分区：
文献类型：
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作者：
Liu, Yong;Tang, Shaoxun;Gonzalez-Diaz, Humberto
细胞表面疏水性（cell surface hydrophobic，CSH）是评价微生物在生物材料表面粘附能力的一种物理化学性质，是生物膜形成和致病的重要环节。本体外发酵试验中，沿着了瘤胃混合微生物的CSH，以及pH、氨氮浓度、中性洗涤纤维消化率、表面张力和比表面积等数据记录。构建了一个包含170，707个输入变量扰动的数据集，分为两个数据块。接下来，开发了预期测量移动平均-机器学习（EMMA-ML）模型，以便预测所有输入变量的扰动后的CSH。EMMA-ML是一种扰动理论方法，结合了预期测量，Box Jenkins算子/移动平均和时间序列分析的思想。已经测试了七种回归方法：多元线性回归，逐步特征选择的广义线性模型，偏最小二乘回归，Lasso回归，弹性网络回归，神经网络回归和随机森林（RF）。使用RF（EMMA-RF模型）获得了最佳回归性能，R平方为0.992。模型分析表明，CSH值高度依赖于洗涤剂纤维消化率、氨氮浓度和细胞表面疏水性在第一时间尺度上的期望值的体外发酵参数。(C)2016爱思唯尔有限公司版权所有。
The cell surface hydrophobicity (CSH) is an assessable physicochemical property used to evaluate the microbial adhesion to the surface of biomaterials, which is an essential step in the microbial biofilm formation and pathogenesis. For the present in vitro fermentation experiment, the CSH of ruminal mixed microbes was considered, along with other data records of pH, ammonia-nitrogen concentration, and neutral detergent fibre digestibility, conditions of surface tension and specific surface area in two different time scales. A dataset of 170,707 perturbations of input variables, grouped into two blocks of data, was constructed. Next, Expected Measurement Moving Average - Machine Learning (EMMA-ML) models were developed in order to predict CSH after perturbations,of all input variables. EMMA-ML is a Perturbation Theory method that combines the ideas of Expected Measurement, Box Jenkins Operators/Moving Average, and Time Series Analysis. Seven regression methods have been tested: Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, Elastic Net regression, Neural Networks regression, and Random Forests (RF). The best regression performance has been obtained with RF (EMMA-RF model) with an R-squared of 0.992. The model analysis has shown that CSH values were highly dependent on the in vitro fermentation parameters of detergent fibre digestibility, ammonia - nitrogen concentration, and the expected values of cell surface hydrophobicity in the first time scale. (C) 2016 Elsevier Ltd. All rights reserved.