Machine Learning to Predict Gut Microbiomes of Agricultural Pests

Abstract

While current efforts to control agricultural insect pests largely focus on the widespread use of insecticides, predicting microbiome composition can provide important data for creating more efficient and long-lasting pest control methods by analysing the pest's food-digesting capacity and resistance to bacteria or viruses. Instead of using computationally expensive techniques, we aim to investigate the dynamics of these microbiome compositions using metagenomic samples taken from fruit flies. In this paper, we propose the three machine learning-based biological models. Firstly, we propose the intrafamilial successor prediction, which predicts the relative abundance of each bacterial family using the past four generations. Next, we propose our interfamilial quantitative prediction, where the model predicts the amount of a given bacterial family in each sample using the amount of all other bacteria present in the sample. Lastly. we propose our interfamilial qualitative prediction, which predicts the relative abundance of each bacterial family within a sample using binary information of all bacterial families. All three models were tested against Least Angle Regression, Random Forest, Elastic-Net, and Lasso. The third approach exhibits promising results by applying a Random Forest with the lowest mean Coefficient of Variance of 1.25. The overall results of this study highlight how complex these dynamic systems are and demonstrate that more computationally efficient methods can characterise them quickly.