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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
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Sequential visibility-graph motifs.

Jacopo Iacovacci1, Lucas Lacasa1

  • 1School of Mathematical Sciences, Queen Mary University of London, Mile End Road, E14NS London, United Kingdom.

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Summary
This summary is machine-generated.

This study introduces sequential visibility-graph motifs to analyze time series dynamics. These motifs offer an efficient method for classifying complex data, distinguishing states like meditation from relaxation in heart-rate data.

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Area of Science:

  • Complex Systems Science
  • Network Science
  • Nonlinear Dynamics
  • Time Series Analysis

Background:

  • Visibility algorithms convert time series into graphs, enabling graph-theoretical analysis of dynamical information encoded in their topology.
  • This approach bridges nonlinear dynamics and network science, offering new analytical tools for complex systems.

Purpose of the Study:

  • To introduce and investigate sequential visibility-graph motifs as substructures within time series graphs.
  • To develop a theoretical framework for computing motif profiles of deterministic and stochastic dynamics.
  • To demonstrate the utility of motif profiles for time series classification and unsupervised learning.

Main Methods:

  • Development of a theory to precisely calculate motif profiles for various deterministic and stochastic dynamics.
  • Analysis of motif profile properties, assessing their informativeness, computational efficiency, and robustness to noise.
  • Application of motif profile extraction for unsupervised learning on experimental heart-rate time series data.

Main Results:

  • Sequential visibility-graph motifs are highly informative and computationally efficient features for distinguishing dynamics.
  • Motif profiles demonstrate robustness against noise contamination in time series data.
  • Unsupervised learning using motif profiles successfully differentiates meditative states from other relaxation states in heart-rate data.

Conclusions:

  • Sequential visibility-graph motifs provide a powerful tool for characterizing and classifying diverse time series.
  • The developed theory enables exact computation of motif profiles, applicable to physical, biological, and financial data.
  • This method facilitates practical applications in automatic time series classification and state identification.