ArticleEvolutionary bioinformatics online2007
Genome signatures, self-organizing maps and higher order phylogenies: a parametric analysis.
Article in Evolutionary bioinformatics online, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Self-organizing maps for allele specific expression data reconstruction and identification of anomalous genomic regions.Frontiers in bioinformatics · 2026Article
- The epigenomic landscape of bronchial epithelial cells reveals the establishment of trained immunity.Genes and immunity · 2025Article
- Article
- LAF: Logic Alignment Free and its application to bacterial genomes classification.BioData mining · 2015Article
- Article
- Whole-proteome phylogeny of large dsDNA virus families by an alignment-free method.Proceedings of the National Academy of Sciences of the United States of America · 2009Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Genome signatures are data vectors derived from the compositional statistics of DNA. The self-organizing map (SOM) is a neural network method for the conceptualisation of relationships within complex data, such as genome signatures. The various parameters of the SOM training phase are investigated for their effect on the accuracy of the resulting output map. It is concluded that larger SOMs, as well as taking longer to train, are less sensitive in phylogenetic classification of unknown DNA sequences. However, where a classification can be made, a larger SOM is more accurate. Increasing the number of iterations in the training phase of the SOM only slightly increases accuracy, without improving sensitivity. The optimal length of the DNA sequence k-mer from which the genome signature should be derived is 4 or 5, but shorter values are almost as effective. In general, these results indicate that small, rapidly trained SOMs are generally as good as larger, longer trained ones for the analysis of genome signatures. These results may also be more generally applicable to the use of SOMs for other complex data sets, such as microarray data.
Indexed as
Identifiers
19468314PMC2684143What 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.