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Variable Size Genetic Network Programming (GNPvs) with Binomial Distribution is a new type of Genetic Network Programming, which permits the individuals with variable size and obtains the optimal size of individuals during evolution. In contrast to swapping the corresponding nodes in the crossover of Genetic Network Programming (GNP), the crossover of GNPvs moves a number of nodes from each parent...
The computational identification from global data sets of stable and predictive patterns of gene and protein relative expression reversals offers a simple, yet powerful approach to target therapies for personalized medicine and to identify pathways that are disease-perturbed. We previously utilized this approach to identify a molecular classifier with near 100% accuracy for differentiating gastrointestinal...
The accumulations of biological data in various knowledge domains (biological themes) provide rich resources for extracting deeper biological insights into biosystems. However, relations among these biological themes remain uncharacterized. Here, we present a systematic approach to the discovery of these relationships using a single human network of gene associations. We first constructed the network...
This paper is aimed to select PD gene based on microarray dataset from multiplex 3D brain gene expression mapping. Firstly, we used a statistical method for the normalization of genes. Secondly, we described a strategy to select PD gene. Dividing data into two subsets, calculating the standard deviation (SD) of gene expression and using order statistics and binomial distribution, we chose among the...
As it is known a precise definition of the Brownian motion involves a measure on the path space, such that it is possible to put the Brownian motion on a firm mathematical foundation. In this paper we refer to an application of asymptotic theory of stochastic differential equations in mathematical genetics. The construction of the Brownian motion as a limit of a rescaled random walk can be generalized...
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