ArticlePeerJ2018
Genomic signal processing for DNA sequence clustering.
Article in PeerJ, 2018. 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 abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Clustering methods for categorical time series and sequences : a scoping review.BMC medical research methodology · 2026Article
- TreeWave: command line tool for alignment-free phylogeny reconstruction based on graphical representation of DNA sequences and genomic signal processing.BMC bioinformatics · 2024Article
- Leveraging machine learning for taxonomic classification of emerging astroviruses.Frontiers in molecular biosciences · 2023Article
- An Information-Entropy Position-WeightedFrontiers in genetics · 2021Article
- Frequency spectra characterization of noncoding human genomic sequences.Genes & genomics · 2020Article
- Review on the Application of Machine Learning Algorithms in the Sequence Data Mining of DNA.Frontiers in bioengineering and biotechnology · 2020Review
- Effective machine-learning assembly for next-generation amplicon sequencing with very low coverage.BMC bioinformatics · 2019Article
- Integrated entropy-based approach for analyzing exons and introns in DNA sequences.BMC bioinformatics · 2019Article
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic signal processing (GSP) methods which convert DNA data to numerical values have recently been proposed, which would offer the opportunity of employing existing digital signal processing methods for genomic data. One of the most used methods for exploring data is cluster analysis which refers to the unsupervised classification of patterns in data. In this paper, we propose a novel approach for performing cluster analysis of DNA sequences that is based on the use of GSP methods and the K-means algorithm. We also propose a visualization method that facilitates the easy inspection and analysis of the results and possible hidden behaviors. Our results support the feasibility of employing the proposed method to find and easily visualize interesting features of sets of DNA data.
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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.