Learnable dendrite neural P systems and applications in survival prediction of glioblastoma patients.

Abstract

Current neural-like P systems use "point neurons" as the computing entities, and the computations in these neurons are simplified, ignoring the fact that, in organisms, subcellular compartments (such as neuronal dendrites) can also perform operations as independent computing units in addition to computing at the individual neuron level. The nervous system has a strong ability for optimization learning. Therefore, we propose learnable dendrite neural P (LDNP) systems with new plasticity rules, in which the dendrite structure and learning function can be adaptively changed when solving different application problems. Specifically, the dendrites of neurons are designed as dendritic trees composed of multiple dendritic branches, each of which serves as an independent computing unit. The multilevel complex topological structure of dendrites provides powerful computing capabilities for neurons. A model for predicting the overall survival of glioblastoma (GBM) patients was developed based on LDNP systems and validated on the GBM cohort from the Cancer Genome Atlas. Compared with thirteen state-of-the-art methods, the LDNP system achieves the best performance.