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This study aimed to perform a comprehensive assessment of the association between CD58 polymorphisms and the risk of neuromyelitis optica spectrum disorders (NMOSD) in a Han Chinese population. Nine single-nucleotide polymorphisms (SNPs) were genotyped in 230 NMOSD patients and 487 healthy controls. Five SNPs were significantly associated with an increased risk of NMOSD (rs2300747, rs1335532, rs56302466,...
The paper provides the method of the improved genetic neural network for image segmentation. The method uses improved genetic algorithm BP neural network weights and thresholds to optimize, and use the definition of bipolar fitness function mapping compression to speed up neural network training speed, and then use iterative improved neural network algorithm to achieve image segmentation. The results...
Particle degeneration is a key issue in the performance of a particle filter. In this paper we introduce genetic Monte Carlo into sampling process with the basic idea of solving particle degeneration by means of evolution thought. It is shown that the novel particle filtering framework can effectively eliminate particle degeneration and reduce its dependency on the particle validity. Furthermore,...
Using particle filter to track human movement, a key problem is how to draw samples in high-dimensional state space. In this paper, we present a novel framework of particle filtering, namely Hierarchical Genetic Particle Filter (HGPF), to improve the efficiency of samples by a hierarchical evolutionary detection. As a result, we can obtain reasonably distributed samples thus translating into reliable...
The construction of genetic regulatory networks from time series gene expression data is an important research topic in bioinformatics as large amounts of quantitative gene expression data can be routinely generated nowadays. One of the main difficulties in building such genetic networks is that the data set has huge number of genes but small number of time points. In this paper, we propose a linear...
The construction and control of genetic regulatory networks using gene expression data is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been served as an effective tool for this purpose. However, PBNs are difficult to be used in practice when the number of genes is large because of the huge computational cost. In this paper, we propose a simplified multivariate...
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