Gating-Enhanced Hierarchical Structure Learning in Hyperbolic Space and Multi-scale Neighbor Topology Learning in Euclidean Space for Prediction of Microbe-Drug Associations

Identifying drug-related microbes may help us explore
how the microbes
affect the functions of drugs by promoting or inhibiting their effects.
Most previous methods for the prediction of microbe-drug associations
focused on integrating the attributes and topologies of microbe and
drug nodes in Euclidean space. The heterogeneous network composed
of microbes and drugs has a hierarchical structure, and the hyperbolic
space is helpful for reflecting the structure. However, the previous
methods did not fully exploit the structure. We propose a multi-space
feature learning enhanced microbe-drug association prediction method,
MFLP, to fuse the hierarchical structure of microbe and drug nodes
in hyperbolic space and the multiscale neighbor topologies in Euclidean
space. First, we project the nodes of the microbe-drug heterogeneous
network on the sphere in hyperbolic space and then construct a topology
which implies hierarchical structure and forms a hierarchical attribute
embedding. The node information from multiple types of neighbor nodes
with the new topological structure in the tangent plane space of a
sphere is aggregated by the designed gating-enhanced hyperbolic graph
neural network. Second, the gate at the node feature level is constructed
to adaptively fuse the hierarchical features of microbe and drug nodes
from two adjacent graph neural encoding layers. Third, multiple neighbor
topological embeddings for each microbe and drug node are formed by
neighborhood random walks on the microbe-drug heterogeneous network,
and they cover neighborhood topologies with multiple scales, respectively.
Finally, as each scale of topological embedding contains its specific
neighborhood topology, we establish an independent graph convolutional
neural network for the topology and form the topological representations
of microbe and drug nodes in Euclidean space. The comparison experiments
based on cross validation showed that MFLP outperformed several advanced
prediction methods, and the ablation experiments verified the effectiveness
of MFLP’s major innovations. The case studies on three drugs
further demonstrated MFLP’s ability in being applied to discover
potential candidate microbes for the given drugs.