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Fairly effective methods exist for finding new non-coding RNA genes using search models based on known families of ncRNA genes (for example covariance models). However, these models only find new members of the existing families and are not useful in finding potential members of novel ncRNA families. Other problems with family-specific search include large processing requirements, ambiguity in defining...
In this paper, we propose a Parallel evolutionary computing model, called CLA-EC. This model is a combination of a model called cellular learning automata (CLA) and the evolutionary model. In this new model, each genome is assigned to a cell of cellular learning automata to each of which a set of learning automata is assigned. The set of actions selected by the set of automata associated to a cell...
Bayesian linear classifier is the basic scheme to solve model classification basing on statistics. Face with the classification of three different nectar plant, the near infrared spectrum data was acquired. The character of the near infrared spectrums is known as litter sample and higher dimension. In this paper, the method has developed to acquire the feature wavelength based on genetic algorithm...
This work presents a methodology for the application of a parallel genetic algorithm (PGA) to the problem of protein folding prediction, using the 3D-HP-side chain model. This model is more realistic than the usual 3D-HP model but, on the other hand, it is has a higher degree of complexity. Specific encoding and fitness function were proposed for this model, and running parameters were experimentally...
Island model genetic algorithms are fast spreading optimization method because of their parallel nature. We focus on the global exploration capabilities of mutation-based algorithms. Using a simple bimodal test function and rigorous runtime analyses, we compare well-known transfer techniques of island model genetic algorithms.
The computing time requirements for using covariance models to search for non-coding RNA genes in genomic data make direct use of these models for RNA search impractical for RNA families with long consensus sequences. A possible solution is to break family models into portions and apply these portions sequentially on a segment of database until either an acceptance or rejection can be obtained on...
The minimum error correction (MEC) model is one of the widely accepted computational model for single individual haplotype reconstruction problem, and it has been proved to be NP-complete by Lippert et al.. Qian et al. presented a particle swarm optimization (PSO) algorithm to solve the model, and the length of a particle code is equal to the number of fragments. However, there are hundreds and thousands...
Metropolis sampling is the earliest Markov chain Monte Carlo (MCMC) method and MCMC has been widely used in motif-finding via sequence local alignment. A key issue in the design of MCMC algorithms is to improve the proposal mechanism and the mixing behavior. To overcome these difficulties, it is common either to run a population of chains or incorporate the evolutionary computing techniques into the...
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