ReviewComputational and structural biotechnology journal2021
Chaos game representation and its applications in bioinformatics.
Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- A unified benchmark of supervised and retrieval-based methods for viral genomic sequence classification.Scientific reports · 2026Article
- Genome-wide pervasiveness and localized variation of [Formula: see text]-mer-based genomic signatures in eukaryotes.Scientific reports · 2026Article
- A novel dynamic part-wise direct and complementary template matching approach for DNA sequence similarity identification.Molecular genetics and genomics : MGG · 2026Article
- Contrastive representation learning and capsule networks enable accurate identification of ferroptosis-related proteins.Journal of cheminformatics · 2026Article
- ANIA: an inception-attention network for predicting minimum inhibitory concentration of antimicrobial peptides.Briefings in bioinformatics · 2026Article
- 20D-Dynamic Representation of Protein Sequences Combined with K-means Clustering.Combinatorial chemistry & high throughput screening · 2026Article
- Predicting VNN resistance in European sea bass using machine learning on high dimensional low sample size data.Frontiers in bioinformatics · 2026Article
- Vertex Assignment for Frequency Chaos Game Representation of Proteins Affects Classification Performance.Computational and structural biotechnology journal · 2026Article
- Identification of mathematical patterns in genomic spectrograms linked to variant classification in complete SARS-CoV-2 sequences.Scientific reports · 2025Article
- Unraveling Plant Recombination Patterns: Insights From Genome k-mers.Plant direct · 2025Article
- From nucleotides to numbers: a comprehensive review of RNA feature extraction methods for computational modelling.Briefings in bioinformatics · 2025Review
- Positional frequency chaos game representation for machine learning-based classification of crop lncRNAs.bioRxiv : the preprint server for biology · 2025Article
- PCVR: a pre-trained contextualized visual representation for DNA sequence classification.BMC bioinformatics · 2025Article
- ProtPhage: a deep learning framework for phage viral protein identification and functional annotation.Briefings in bioinformatics · 2025Article
- Multifractal analysis and support vector machine for the classification of coronaviruses and SARS-CoV-2 variants.Scientific reports · 2025Article
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- Complete end-to-end learning from protein feature representation to protein interactome inference.GigaScience · 2025Article
- Exploration of chaos game representation and integrative deep learning approaches for whole-genome sequencing-based grapevine genetic testing.Bioinformatics advances · 2025Article
- CAP-m7G: A capsule network-based framework for specific RNA N7-methylguanosine site identification using image encoding and reconstruction layers.Computational and structural biotechnology journal · 2025Article
- CGRclust: Chaos Game Representation for twin contrastive clustering of unlabelled DNA sequences.BMC genomics · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chaos game representation (CGR), a milestone in graphical bioinformatics, has become a powerful tool regarding alignment-free sequence comparison and feature encoding for machine learning. The algorithm maps a sequence to 2-dimensional space, while an extension of the CGR, the so-called frequency matrix representation (FCGR), transforms sequences of different lengths into equal-sized images or matrices. The CGR is a generalized Markov chain and includes various properties, which allow a unique representation of a sequence. Therefore, it has a broad spectrum of applications in bioinformatics, such as sequence comparison and phylogenetic analysis and as an encoding of sequences for machine learning. This review introduces the construction of CGRs and FCGRs, their applications on DNA and proteins, and gives an overview of recent applications and progress in bioinformatics.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.