Abstract
Traditional studies are mostly based on static protein network models. The highly averaged and idealized static protein network does not represent well this dynamic nature that encompasses different time, place and reaction conditions. Therefore, we propose PreDPPI, a method for convolving dynamic protein-protein interaction networks (PINs) using knowledge graph reconstruction of relational graphs, which achieves better performance in terms of dynamics compared to other methods. We found that the longer the length of timestamps used in reconstructing dynamic PINs, the better the prediction. In addition, we identified three hub genes (NUSAP1, SCG3, and CKAP2L) in dynamic PINs that were shown to be significantly associated with glioma prognosis. In conclusion, this work reconstructed a web-based knowledge graph convolving dynamic PINs and identified three hub genes in the GEO glioma dataset that were confirmed to be highly associated with glioma prognosis.
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