Chaos game representation and its applications in bioinformatics,Computational and Structural Biotechnology Journal

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Chaos game representation and its applications in bioinformatics
Computational and Structural Biotechnology Journal ( IF 4.4 ) Pub Date : 2021-11-10 , DOI: 10.1016/j.csbj.2021.11.008
Hannah Franziska Löchel ₁ , Dominik Heider ₁

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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.

中文翻译：

混沌博弈表示及其在生物信息学中的应用

混沌游戏表示（CGR）是图形生物信息学的一个里程碑，已成为机器学习的免对齐序列比较和特征编码的强大工具。该算法将序列映射到二维空间，而 CGR 的扩展，即所谓的频率矩阵表示（FCGR），将不同长度的序列转换为相同大小的图像或矩阵。 CGR 是广义马尔可夫链，包含各种属性，允许序列的唯一表示。因此，它在生物信息学中具有广泛的应用，例如序列比较和系统发育分析以及作为机器学习的序列编码。本文介绍了CGRs和FCGRs的构建、它们在DNA和蛋白质上的应用，并概述了生物信息学的最新应用和进展。

更新日期：2021-11-10

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