ArticleComputational and structural biotechnology journal2020
A novel numerical representation for proteins: Three-dimensional Chaos Game Representation and its Extended Natural Vector.
Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Computational function prediction of bacteria and phage proteins.Microbiology and molecular biology reviews : MMBR · 2025Review
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- On leveraging self-supervised learning for accurate HCV genotyping.Scientific reports · 2024Article
- CGRWDL: alignment-free phylogeny reconstruction method for viruses based on chaos game representation weighted by dynamical language model.Frontiers in microbiology · 2024Article
- An accurate alignment-free protein sequence comparator based on physicochemical properties of amino acids.Scientific reports · 2022Article
- Detection of intra-family coronavirus genome sequences through graphical representation and artificial neural network.Expert systems with applications · 2022Article
- Clustering and classification of virus sequence through music communication protocol and wavelet transform.Genomics · 2021Article
- Chaos game representation and its applications in bioinformatics.Computational and structural biotechnology journal · 2021Review
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4 authors.
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Abstract
Chaos Game Representation (CGR) was first proposed to be an image representation method of DNA and have been extended to the case of other biological macromolecules. Compared with the CGR images of DNA, where DNA sequences are converted into a series of points in the unit square, the existing CGR images of protein are not so elegant in geometry and the implications of the distribution of points in the CGR image are not so obvious. In this study, by naturally distributing the twenty amino acids on the vertices of a regular dodecahedron, we introduce a novel three-dimensional image representation of protein sequences with CGR method. We also associate each CGR image with a vector in high dimensional Euclidean space, called the extended natural vector (ENV), in order to analyze the information contained in the CGR images. Based on the results of protein classification and phylogenetic analysis, our method could serve as a precise method to discover biological relationships between proteins.
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