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Complexity metrics and machine learning (ML) models have been utilized toanalyze the lengths of segmental genomic entities like: exons, introns,intergenic and repeat/unique DNA sequences, in each of the 22 humanchromosomes. The purpose of the study was to assess information and order thatmay be concealed within the size distribution of these sequences. For thispurpose, we developed an innovative integrated methodology. Our analysis isbased upon the reconstructed phase space theorem, the non-extensive statisticaltheory of Tsallis, ML techniques and a new technical index, integrating thegenerated information, which we introduce and named it Complexity Factor(COFA). The low-dimensional deterministic nonlinear chaotic and non-extensivestatistical character of the DNA sequences was verified with strongmultifractal characteristics and long-range correlations with significantvariations per genomic entity and per chromosome. The results of the analysisreveal changes in complexity behavior per genomic entity and chromosomeregarding the size distribution of individual genomic segment. The lengths ofintron regions show greater complexity behavior in all metrics than the exonicones, with longer range correlations, and stronger memory effects, for allchromosomes. We conclude from our analysis, that the size distribution of thegenomic regions within chromosomes, are not random, but follow a specificpattern with characteristic features, that have been seen here through itscomplexity character, and it is part of the dynamics of the whole genomeaccording to complexity theory. This picture of dynamics of the redundancy ofinformation in DNA recognized from ML tools for clustering, classification andprediction.

Information and order of genomic sequences within chromosomes as identified by complexity theory. An integrated methodology