Genome signatures, self-organizing maps and higher order phylogenies: a parametric analysis.

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

SUBMITTER: Gatherer D 

PROVIDER: S-EPMC2684143 | biostudies-literature | 2007 Sep

REPOSITORIES: biostudies-literature