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Identifying anticancer peptides by using a generalized chaos game representation.

Li Ge1, Jiaguo Liu1, Yusen Zhang2

  • 1School of Mathematics and Statistics, Shandong University at Weihai, Weihai, 264209, China.

Journal of Mathematical Biology
|October 7, 2018
PubMed
Summary
This summary is machine-generated.

We generalized chaos game representation (CGR) to higher dimensions, creating a robust method for analyzing sequence similarity and extracting features. This approach significantly improves anticancer peptide prediction models.

Keywords:
Anticancer peptidesChaos game representationSimilarity analysisSupport vector machine

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Sequence Analysis

Background:

  • Chaos Game Representation (CGR) is a method for visualizing fractal patterns in sequences.
  • Previous CGR methods lacked rigor and representation in higher dimensions.
  • An asymptotic property of CGR and its generalization was not previously understood.

Purpose of the Study:

  • To generalize CGR to higher dimensions while maintaining bijection and mathematical rigor.
  • To identify and leverage the exponential effect of identical subsequences on sequence dissimilarity.
  • To develop novel feature extraction techniques for sequence analysis and biological predictions.

Main Methods:

  • Generalization of Chaos Game Representation (GCGR) to higher dimensions.
  • Mathematical proof of the asymptotic property for CGR and GCGR.
  • Development of GCGR-Centroid and GCGR-Variance feature extraction techniques.
  • Analysis of protein sequence similarity using GCGR-Centroid.
  • Training of anticancer peptide prediction models using support vector machines with GCGR features.

Main Results:

  • GCGR maintains bijection and mathematical rigor in higher dimensions.
  • Identical subsequences exponentially decrease sequence dissimilarity, supporting GCGR as a similarity measure.
  • GCGR-Centroid analysis of protein sequences yielded consistent results with previous studies.
  • Anticancer peptide prediction models trained with GCGR-Centroid and GCGR-Variance showed significantly higher performance.

Conclusions:

  • Generalized Chaos Game Representation (GCGR) offers a powerful and rigorous method for higher-dimensional sequence analysis.
  • GCGR-based feature extraction techniques are effective for analyzing biological sequence similarity.
  • GCGR significantly enhances the performance of predictive models, such as those for anticancer peptides.