Back

Complexity And Ergodicity In Chaos Game Representation Of Genomic Sequences

Mehrpooya, A.; Tavassoly, I.

2023-12-30 bioinformatics
10.1101/2023.12.30.573653 bioRxiv
Show abstract

AO_SCPLOWBSTRACTC_SCPLOWThe Chaos Game Representation (CGR) serves as a powerful graphical tool for transforming long one-dimensional sequences, such as genomic sequences, into visually insightful and complex graphical patterns. This algorithmic approach has revealed captivating fractal properties, providing a systems-level perspective on the underlying patterns inherent in specific genomic sequences. Each CGR image generated is distinctive and unique, capturing the individuality of different genomic sequences. We have investigated the nature of the fractal patterns discerned within CGR of genomic sequences, aiming to assess the ergodicity and identify potential ergodic patterns. Our findings present a mathematical proof establishing that CGRs are non-ergodic. This finding suggests that the distribution properties of nucleotide sequences play a pivotal role in shaping the emergent patterns within the CGR images.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 17%
10.6%
Similar papers in this journal
2
Physical Review E
● 112 papers in training set
Top 0.1%
7.9%
Similar papers in this journal
3
Physical Biology
○ 46 papers in training set
Top 0.1%
5.2%
Similar papers in this journal
4
Bioinformatics
◐ 1204 papers in training set
Top 4%
5.2%
Similar papers in this journal
5
Physica A: Statistical Mechanics and its Applications
○ 13 papers in training set
Top 0.1%
4.3%
Similar papers in this journal
6
Scientific Reports
○ 3612 papers in training set
Top 22%
4.3%
7
Journal of Computational Biology
○ 48 papers in training set
Top 0.2%
4.3%
8
Journal of The Royal Society Interface
● 235 papers in training set
Top 1%
4.0%
9
BMC Bioinformatics
○ 457 papers in training set
Top 2%
4.0%
10
PLOS Computational Biology
● 1863 papers in training set
Top 9%
3.4%
50% of probability mass above
11
Journal of Theoretical Biology
○ 162 papers in training set
Top 0.8%
3.4%
12
Biosystems
○ 31 papers in training set
Top 0.1%
3.2%
13
Royal Society Open Science
● 214 papers in training set
Top 2%
2.8%
14
Entropy
○ 21 papers in training set
Top 0.1%
2.8%
15
Mathematical Biosciences
○ 49 papers in training set
Top 0.5%
2.1%
16
Frontiers in Molecular Biosciences
○ 102 papers in training set
Top 0.7%
1.7%
17
Briefings in Bioinformatics
◐ 354 papers in training set
Top 4%
1.7%
18
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 4%
1.7%
19
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.5%
1.5%
20
IEEE/ACM Transactions on Computational Biology and Bioinformatics
● 38 papers in training set
Top 0.6%
1.5%
21
Frontiers in Applied Mathematics and Statistics
○ 11 papers in training set
Top 0.3%
1.1%
22
International Journal of Molecular Sciences
○ 494 papers in training set
Top 12%
1.1%
23
IEEE Access
● 35 papers in training set
Top 1%
1.0%
24
Life
○ 29 papers in training set
Top 0.9%
0.8%
25
Theoretical Population Biology
○ 50 papers in training set
Top 0.4%
0.6%
26
BioData Mining
○ 22 papers in training set
Top 0.8%
0.6%
27
Biophysical Journal
○ 631 papers in training set
Top 5%
0.6%
28
Frontiers in Genetics
○ 230 papers in training set
Top 6%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.