Machine learning of microbial interactions using abductive ILP and hypothesis frequency/compression estimation

Interaction between species in microbial communities plays an important role in the functioning of all ecosystems, from cropland soils to human gut microbiota. Many statistical approaches have been proposed to infer these interactions from microbial abundance information. However, these statistical approaches have no general mechanisms for incorporating existing ecological knowledge in the inference process. We propose an Abductive/Inductive Logic Programming (A/ILP) framework to infer microbial interactions from microbial abundance data, by including logical descriptions of different types of interaction as background knowledge in the learning. This framework also includes a new mechanism …