Kavli Affiliate: Yi Zhou
| First 5 Authors: Yi Zhou, Meixuan Wu, Chengzhou Ouyang, Xinyi Wang, Min Zhu
| Summary:
Biomedical studies have 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). The generalizability is a significant issue,
the prediction ought to be available for entities with fewer or without
existing MDAs, while it is previously underemphasized. In this study, we work
on the stages of data, model, and result analysis. First, we integrate
multi-source data into a miRNA-PCG-disease graph, embracing all authoritative
recorded human miRNAs and diseases, and the verified MDAs are split by time and
known degree as a benchmark. Second, we propose an end-to-end data-driven model
that avoids taking the existing MDAs as an input feature. It performs node
feature encoding, graph structure learning, and binary prediction centered on a
heterogeneous graph transformer. Finally, computational experiments indicate
that our method achieves state-of-the-art performance on basic metrics and
effectively alleviates the neglect of less and zero known miRNAs and diseases.
Predictions are conducted on all human miRNA-disease pairs, case studies
further demonstrate that we can make reliable MDA detections on unseen
diseases, and the prediction basis is instance-level explainable.
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