Machine learning to predict gut microbiomes of agricultural pests

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. We aim to develop a machine learning model to predict the microbiome composition in agricultural pests and investigate the dynamics of these microbiome compositions using metagenomic samples taken from fruit flies. In this paper, we propose three machine learning-based biological models. Firstly, we propose an intrafamilial model that predicts the relative abundance of bacterial families within themselves using their past generations. Next, we propose two interfamilial models following quantitative and qualitative approaches. The quantitative model predicts the number of bacterial families in a given sample based on the presence of other families in that sample. The qualitative model predicts the relative abundance 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. The results of this study are intended to be used as a tool to identify vital taxological families, genera and species of the potential microbiome for better pest control.