On cluster tree for nested and multi-density data clustering
Pattern Recognition
Topology preserving SOM with transductive confidence machine
DS'10 Proceedings of the 13th international conference on Discovery science
Theoretical aspects of mapping to multidimensional optimal regions as a multi-classifier
Intelligent Data Analysis
| Hi-index | 3.84 |
Motivation: Modern high-throughput sequencing technologies enable the simultaneous analysis of organisms in an environment. The analysis of species diversity and the binning of DNA fragments of non-sequenced species for assembly are two major challenges in sequence analysis. To achieve reasonable binnings and classifications, DNA fragment structure has to be represented appropriately, so it can be processed by machine learning algorithms. Results: Hierarchically growing hyperbolic Self-Organizing maps (H 2SOMs) are trained to cluster small variable-length DNA fragments (0.2–50 kb) of 350 prokaryotic organisms at six taxonomic ranks Superkingdom, Phylum, Class, Order, Genus and Species in the Tree of Life. DNA fragments are mapped to three different types of feature vectors based on the genomic signature: basic features, features considering the importance of oligonucleotide patterns as well as contrast enhanced features. The H 2SOM classifier achieves high classification rates while at the same time its visualization allows further insights into the projected data and has the potential to support binning of short sequence reads, because DNA fragments can be grouped into phylogenetic groups. Availability: An implementation of the H 2HSOM classifier in Matlab is provided at www.techfak.uni-bielefeld.de/ags/ani/projects/HHSOMSeqData Contact: christian.martin@uni-bielefeld.de Supplementary Information:Supplementary data are available at Bioinformatics online.