Experimental study and Random Forest prediction model of microbiome cell surface hydrophobicity

作者：

Highlights：

• Experimental study and prediction model of microbiome cell surface hydrophobicity.

• Expected Measurement Moving Average – Machine Learning model to predict CSH.

• Random Forest prediction model with 12 features and test R-squared of 0.992.

摘要

•Experimental study and prediction model of microbiome cell surface hydrophobicity.•Expected Measurement Moving Average – Machine Learning model to predict CSH.•Random Forest prediction model with 12 features and test R-squared of 0.992.

论文关键词：Machine learning,Expected values,Moving averages,Cell properties,Perturbation theory,Time series analysis

论文评审过程：Received 14 July 2016, Revised 27 October 2016, Accepted 27 October 2016, Available online 9 November 2016, Version of Record 2 January 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.10.058