The innovative model of partial array languages generated by basic puzzle partial array grammars is available in the literature. Here we define Partial array Token Petri Net Structure (PATPNS) to generate partial array languages. Further we introduce Partial Array Token Petri Net P System (PATPNPS) to generate partial array languages and compared with basic puzzle partial array grammars for generative power. PATPNS is also compared with local and recognizable partial array languages.
Siromoney et al. introduced a parallel/sequential generative model called Tabled Matrix Grammars (TMGs) by generalising phrase structure matrix grammars generating abstract families of languages (AFLs). James et al. introduced Parallel Contextual Array Insertion Deletion P Systems (PCAIDPSs) to generate two-dimensional array languages using insertion and deletion operations through parallel contextual mappings. In this paper, we compare the generative powers of PCAIDPSs and TMGs. We prove that the family of languages generated by PCAIDPS with two membranes properly includes the family of languages generated by Tabled Context-sensitive Matrix Grammars (TCSMGs).
Selection of relevant genes is the crucial task for sample classification in microarray data, where researchers try to identify the smallest possible set of genes that can still achieve good predictive performance. Due to the problem of higher risk of overfitting in wrapper methods and sensitivity of the best embedded way to filter out factor that leads to unstable model and significantly different gene subsets, in this paper, we propose a novel model for evaluating and improving techniques for selecting informative genes from microarray data. This model inspired by membrane computing and used the kernel P system (kP) as the variant of the P system to improve the performance of the intelligent algorithm, multi-objective binary particle swarm optimization (MObPSO). The proposed model consists of two main parts. First, kP-MObPSO, which resembles a wrapper type feature selection, and the second part that improves the results of the first part through an embedded feature selection and classification idea based on the kP system. Division, rewriting, and input/output rules are used to make interaction among the genes inside and between the particles. The proposed model applied to the colorectal and breast dataset contains 100 genes with six attributes. The embedded part of the model extracts the marker gene sets indicate more stability and reliability based on ROC measure as well as better error rate in comparison to the wrapper part of the model. In the paper, the lowest error rate by an embedded model is displayed as 0.1111 for breast cancer and 0.0769 for colorectal data.
We introduce and examine two variants of networks of reaction systems, called communicating reaction systems with direct communication, where the reaction systems send products or reactions to each other. We show that these types of networks of reaction systems can be obtained by simple mappings from single reaction systems. We also discuss some aspects of communication within these networks, and suggest open problems for future research.
We study the transition graphs, and thus, the possible computational paths of reaction systems which are reversible according to different notions of reversibility. We show that systems which are reversible in the sense of our earlier work produce very simple types of transition graphs. A somewhat more complicated, but still quite simple class of transition graphs is obtained if we consider so-called initialized reversible systems. Finally we introduce the notion of reversibility with lookbehind, and show that systems which are reversible in this sense produce the same transition graphs (and thus, the same computations) as the state transition diagrams of reversible finite transition systems.
