ArticleJournal of mathematical biology2019
Identifying anticancer peptides by using a generalized chaos game representation.
Article in Journal of mathematical biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The trial behind it
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Who cites it
9 citing papers in PubMed.
- GPCR systems coordinate cellular resilience against aging-associated stress.Frontiers in molecular biosciences · 2026Review
- On leveraging self-supervised learning for accurate HCV genotyping.Scientific reports · 2024Article
- Microbial characterization based on multifractal analysis of metagenomes.Frontiers in cellular and infection microbiology · 2023Article
- ACP-ADA: A Boosting Method with Data Augmentation for Improved Prediction of Anticancer Peptides.International journal of molecular sciences · 2022Article
- To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification.Sensors (Basel, Switzerland) · 2022Article
- GPCRs Are Optimal Regulators of Complex Biological Systems and Orchestrate the Interface between Health and Disease.International journal of molecular sciences · 2021Review
- ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation.Frontiers in genetics · 2021Article
- A chaotic viewpoint-based approach to solve haplotype assembly using hypergraph model.PloS one · 2020Article
- Encodings and models for antimicrobial peptide classification for multi-resistant pathogens.BioData mining · 2019Review
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
Indexed as
Identifiers
30291366What OpenQuestion holds
Registered trials
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.