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Journal of Mathematical Biology

, Volume 78, Issue 1–2, pp 441–463 | Cite as

Identifying anticancer peptides by using a generalized chaos game representation

  • Li Ge
  • Jiaguo Liu
  • Yusen ZhangEmail author
  • Matthias Dehmer
Article

Abstract

We generalize chaos game representation (CGR) to higher dimensional spaces while maintaining its bijection, keeping such method sufficiently representative and mathematically rigorous compare to previous attempts. We first state and prove the asymptotic property of CGR and our generalized chaos game representation (GCGR) method. The prediction follows that the dissimilarity of sequences which possess identical subsequences but distinct positions would be lowered exponentially by the length of the identical subsequence; this effect was taking place unbeknownst to researchers. By shining a spotlight on it now, we show the effect fundamentally supports (G)CGR as a similarity measure or feature extraction technique. We develop two feature extraction techniques: GCGR-Centroid and GCGR-Variance. We use the GCGR-Centroid to analyze the similarity between protein sequences by using the datasets 9 ND5, 24 TF and 50 beta-globin proteins. We obtain consistent results compared with previous studies which proves the significance thereof. Finally, by utilizing support vector machines, we train the anticancer peptide prediction model by using both GCGR-Centroid and GCGR-Variance, and achieve a significantly higher prediction performance by employing the 3 well-studied anticancer peptide datasets.

Keywords

Chaos game representation Similarity analysis Anticancer peptides Support vector machine 

Mathematics Subject Classification

92-08 

Notes

Acknowledgements

We gratefully acknowledge the anonymous reviewers who read our paper and gave some constructive comments. This work is supported by the National Natural Science Foundation of China (Nos. 61877064). Matthias Dehmer thanks the Austrian Science Funds for supporting this work (project P26142).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mathematics and StatisticsShandong University at WeihaiWeihaiChina
  2. 2.Department of Mechatronics and Biomedical Computer ScienceUMITHall in TyrolAustria

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