Kavli Affiliate: Yi Zhou
| First 5 Authors: Yi Zhou, Meixuan Wu, Chengzhou Ouyang, Min Zhu,
| Summary:
Biomedical research has revealed the crucial role of miRNAs in the
progression of many diseases, and computational prediction methods are
increasingly proposed for assisting biological experiments to verify
miRNA-disease associations (MDAs). However, the generalizability and
explainability are currently underemphasized. It’s significant to generalize
effective predictions to entities with fewer or no existing MDAs and reveal how
the prediction scores are derived. In this study, our work contributes to data,
model, and result analysis. First, for better formulation of the MDA issue, we
integrate multi-source data into a heterogeneous graph with a broader learning
and prediction scope, and we split massive verified MDAs into independent
training, validation, and test sets as a benchmark. Second, we construct an
end-to-end data-driven model that performs node feature encoding, graph
structure learning, and binary prediction sequentially, with a heterogeneous
graph transformer as the central module. Finally, computational experiments
illustrate that our method outperforms existing state-of-the-art methods,
achieving better evaluation metrics and alleviating the neglect of unknown
miRNAs and diseases effectively. Case studies further demonstrate that we can
make reliable MDA detections on diseases without MDA records, and the
predictions can be explained in general and case by case.
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