Hypergraph-based spiking neural P systems for predicting the overall survival time of glioblastoma patients

Spiking neural P (SN P) systems are membrane computing models inspired by the information interaction of spikes among neurons. Although real neurons have complex structures, classical SN P systems are two-dimensional graph structures. Neurons can only communicate in plane, which limits the learning ability of SN P systems in solving practical problems. To solve this issue, we propose in this paper hypergraph-based SN P (HSN P) systems containing three new classes of neurons to describe higher-order relationships among neurons. Three new kinds of rules among neurons are also designed to expand the model into planar, hierarchical and transmembrane computations. Based on the hypergraph-based spiking neural P systems, a new model for predicting the overall survival (OS) time of glioblastoma (GBM) patients is developed. The proposed model is evaluated on GBM cohorts from The Cancer Genome Atlas (TCGA-GBM). The HSN P system achieves good performance compared to the six state-of-the-art methods, thereby verifying the effectiveness of the model in predicting the OS time of GBM patients.

Automated summary

The paper proposes a hypergraph-based SN P (HSN P) system to describe higher-order relationships among neurons. New rules among neurons are designed to expand the model into planar, hierarchical and transmembrane computations. A new model for predicting the overall survival (OS) time of glioblastoma (GBM) patients is developed based on the HSN P system and evaluated on TCGA-GBM dataset. The HSN P system achieves good performance compared to the six state-of-the-art methods, verifying its effectiveness in predicting the OS time of GBM patients.