ArticleFrontiers in genetics2019
A Novel Approach to Clustering Genome Sequences Using Inter-nucleotide Covariance.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Machine Learning Classification of Prostate Cancer Genomic Sequences Using K-Mer and Sequence-Derived Features.Computational molecular bioscience · 2026Article
- Unsupervised identification of significant lineages of SARS-CoV-2 through scalable machine learning methods.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- dna2bit: high performance genomic distance estimation software for microbial genome analysis.Frontiers in microbiology · 2024Article
- Comparative study of encoded and alignment-based methods for virus taxonomy classification.Scientific reports · 2023Article
- Article
- GenomeBits Characterization of MPXV.Genes · 2022Article
- Classification of Protein Sequences by a Novel Alignment-Free Method on Bacterial and Virus Families.Genes · 2022Article
- Protein-protein interaction and non-interaction predictions using gene sequence natural vector.Communications biology · 2022Article
- Review
- Article
- Clustering and classification of virus sequence through music communication protocol and wavelet transform.Genomics · 2021Article
- Full Chromosomal Relationships Between Populations and the Origin of Humans.Frontiers in genetics · 2021Article
- Geometric construction of viral genome space and its applications.Computational and structural biotechnology journal · 2021Article
- Whole-GenomeGenes · 2020Article
- Circular Clustering With Polar Coordinate Reconstruction.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Classification of DNA sequences is an important issue in the bioinformatics study, yet most existing methods for phylogenetic analysis including Multiple Sequence Alignment (MSA) are time-consuming and computationally expensive. The alignment-free methods are popular nowadays, whereas the manual intervention in those methods usually decreases the accuracy. Also, the interactions among nucleotides are neglected in most methods. Here we propose a new Accumulated Natural Vector (ANV) method which represents each DNA sequence by a point in ℝ
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