Chaotic Motif Sampler for Motif Discovery Using Statistical Values of Spike Time-Series

Authors:
Takafumi Matsuura;Tohru Ikeguchi
Affiliations:
Graduate School of Science and Engineering, Saitama University, Saitama-city, Japan 338-8570;Graduate School of Science and Engineering, Saitama University, Saitama-city, Japan 338-8570
Venue:
Neural Information Processing
Year:
2007

Citing 4
Cited 0

On approximation algorithms for local multiple alignment

RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Solving large scale traveling salesman problems by chaotic neurodynamics

Neural Networks
Differences in spiking patterns among cortical neurons

Neural Computation
Refractory effects of chaotic neurodynamics for finding motifs from DNA sequences

IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

One of the most important issues in bioinformatics is to discover a common and conserved pattern, which is called a motif, from biological sequences. We have already proposed a motif extraction method called Chaotic Motif Sampler (CMS) by using chaotic dynamics. Exploring a searching space with avoiding undesirable local minima, the CMS discovers the motifs very effectively. During a searching process, chaotic neurons generate very complicated spike time-series. In the present paper, we analyzed the complexity of the spike time-series observed from each chaotic neuron by using a statistical measure, such as a coefficient of variation and a local variation of interspike intervals, which are frequently used in the field of neuroscience. As a result, if a motif is embedded in a sequence, corresponding spike time-series show characteristic behavior. If we use these characteristics, multiple motifs can be identified.