首页> 外文会议>Intelligent Data Engineering and Automated Learing(IDEAL 2006); Lecture Notes in Computer Science; 4224 >Refractory Effects of Chaotic Neurodynamics for Finding Motifs from DNA Sequences
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Refractory Effects of Chaotic Neurodynamics for Finding Motifs from DNA Sequences

机译:从DNA序列中查找基序的混沌神经动力学的难治性作用

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To discover a common and conserved pattern, or motif, from DNA sequences is an important step to analyze DNA sequences because the patterns are acknowledged to reflect biological important information. However, it is difficult to discover unknown motifs from DNA sequences because of its huge number of combination. We have already proposed a new effective method to extract the motifs using a chaotic search, which combines a heuristic algorithm and a chaotic dynamics. To realize the chaotic search, we used a chaotic neural network. The chaotic search exhibits higher performance than conventional methods. Although we have indicated that the refractory effects realized by the chaotic neural network have an essential role, we did not clarify why the refractory effects are important to search optimal solutions. In this paper, we further investigate this issue and reveal the validity of the refractory effects of the chaotic dynamics using surrogate refractory effects. As a result, we discovered that it is important for searching optimal solutions to increase strength of the refractory effects after a firing of neurons.
机译:从DNA序列中发现一个共同且保守的模式或基序是分析DNA序列的重要步骤,因为该模式被认为反映了生物学上的重要信息。然而,由于其大量的结合,很难从DNA序列中发现未知的基序。我们已经提出了一种新的有效方法来使用混沌搜索提取图案,该方法结合了启发式算法和混沌动力学。为了实现混沌搜索,我们使用了混沌神经网络。混沌搜索表现出比常规方法更高的性能。尽管我们已经表明,混沌神经网络实现的不应变作用具有重要作用,但我们并未阐明为什么难变作用对于搜索最优解很重要。在本文中,我们将进一步研究该问题,并使用替代耐火效应来揭示混沌动力学的耐火效应的有效性。结果,我们发现对于寻找最佳解决方案以增加神经元发射后难治性作用的强度很重要。

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