Efficient Examination of Soil Bacteria Using Probabilistic Graphical Models

This paper describes a novel approach to study bacterial relationships in soil datasets using probabilistic graphical models. We demonstrate how to access and reformat publicly available datasets in order to apply machine learning techniques. We first learn a Bayesian network in order to read independencies in linear time between bacterial community characteristics. These independencies are useful in understanding the semantic relationships between bacteria within communities. Next, we learn a Sum-Product network in order to perform inference in linear time. Here, inference can be conducted to answer traditional queries, involving posterior probabilities, or MPE queries, requesting the most likely values of the non-evidence variables given evidence. Our results extend the literature by showing that known relationships between soil bacteria holding in one or a few datasets in fact hold across at least 3500 diverse datasets. This study paves the way for future large-scale studies of agricultural, health, and environmental applications, for which data are publicly available.