Neighborhood Topology-Aware Knowledge Graph Learning and Microbial Preference Inferring for Drug-Microbe Association Prediction

The human microbiota may influence the effectiveness
of drug therapy
by activating or inactivating the pharmacological properties of drugs.
Computational methods have demonstrated their ability to screen reliable
microbe-drug associations and uncover the mechanism by which drugs
exert their functions. However, the previous prediction methods failed
to completely exploit the neighborhood topologies of the microbe and
drug entities and the diverse correlations between the microbe-drug
entity pair and the other entities. In addition, they ignored the
case that a microbe prefers to associate with its own specific drugs.
A novel prediction method, PCMDA, was proposed by learning the neighborhood
topologies of entities, inferring the association preferences, and
integrating the features of each entity pair based on multiple biological
premises. First, a knowledge graph consisting of microbe, disease,
and drug entities is established to help the subsequent integration
of the topological structure of entities and the similarity, interaction,
and association relationship between any two entities. We generate
various topological embeddings for each microbe (or drug) entity through
random walks with neighborhood restarts on the microbe-disease-drug
knowledge graph. Distance-level attention is designed to adaptively
fuse neighborhood topologies covering multiple ranges. Second, the
topological embeddings of entities imply the latent topological relationships
between entities, while the relational embeddings of entities are
derived from the semantics of connections among the entities. The
topological structure and relational semantics of entities are fused
by a designed knowledge graph learning module based on multilayer
perceptron networks. Third, considering the preference that each microbe
tends to especially associate with a group of drugs, information-level
attention is designed to integrate the dependency between microbial
preference and the candidate drug. Finally, a dual-gated network is
established to encode the features of a microbe-drug entity pair from
multiple biological perspectives. The comparative experiments with
seven state-of-the-art methods demonstrate PCMDA’s superior
performance for microbe-drug association prediction. The case studies
on three drugs and the recall rate evaluation for the top-ranked candidates
indicate that PCMDA has the capability of discovering reliable candidate
microbes associated with a drug. The datasets and source codes are
freely available at https://github.com/pingxuan-hlju/pcmda.