Hypergraph-based spiking neural P systems for predicting the overall survival time of glioblastoma patients. (1st April 2023)

• Record Type: Journal Article
• Authors: Dai, Jinpeng; Qi, Feng; Gong, Guanzhong; Liu, Xiyu; Li, Dengwang; Xue, Jie
• Is Part Of: Expert systems with applications. Volume 215(2023)
• Publication Date: 2023-04-01
• Subjects: Spiking neural P systems -- Hypergraph -- Overall survival time prediction -- Glioblastoma -- Histopathology; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.119234
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: 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. Highlights: A hypergraph-based spiking neural P (HSN P) system is proposed. New kinds of neurons and rules are proposed to describe higher-order computation. Efficient and accurate OS prediction model by HSN P system is designed.