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T-Pattern Detection and Analysis (TPA) With THEMETM: A Mixed Methods Approach.

Magnus S Magnusson1

  • 1Human Behavior Laboratory, School of Health Sciences, University of Iceland, Reykjavik, Iceland.

Frontiers in Psychology
|January 31, 2020
PubMed
Summary

This study introduces T-patterns, hierarchical, self-similar structures found across biological scales. T-pattern detection and analysis (TPA) reveals hidden patterns in everything from human behavior to DNA molecules.

Keywords:
T-patternbehaviorethologyfractalinteractionpattern detectionsoftware

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Area of Science:

  • Ethology
  • Computational Biology
  • Bioinformatics

Background:

  • Inspired by ethological studies of animal and human behavior, and linguistic theories.
  • Previous research focused on direct observation and coding of behaviors, with early computational approaches in AI and statistics.
  • The concept of T-patterns emerged from analyzing temporal and spatial structures in nature.

Purpose of the Study:

  • To introduce and detail the T-pattern hypothesis and its detection algorithms.
  • To demonstrate the applicability of T-patterns across diverse biological scales and phenomena.
  • To integrate qualitative and quantitative methods for analyzing complex biological data.

Main Methods:

  • Development of T-pattern detection algorithms, initially using evolution algorithms in THEME software.
  • Utilizing advanced software with parallel processing for efficient detection and analysis.
  • Application of T-pattern detection and analysis (TPA) to various biological data, including human behavior, neuronal activity, and DNA sequences.

Main Results:

  • Detection of hierarchical, self-similar fractal-like structures (T-patterns) in biological systems.
  • Demonstration of T-pattern recurrence across vastly different scales, from molecular (DNA, proteins) to behavioral (human interactions, neuronal networks).
  • Successful integration of qualitative and quantitative analysis through TPA, revealing hidden structures.

Conclusions:

  • T-patterns represent a fundamental organizational principle in biological systems, exhibiting self-similarity across multiple levels.
  • TPA provides a powerful tool for uncovering complex, hidden structures in diverse biological data.
  • The methodology has broad implications for understanding biological organization from genes to complex behaviors.