Abstract
This paper describes a novel approach for annotating metagenomic libraries obtained from environmental samples utilising the self organising map (SOM) neural network formalism. A parallel implementation of the SOM is presented and its particular usefulness in metagenomic annotation highlighted. The benefits of the parallel algorithm and performance increases are explained, the latest results from annotation on an artificially generated metagenomic library presented and the viability of this approach for implementation on existing metagenomic libraries is assessed.
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McCoy, N., Mahony, S., Golden, A. (2007). Gene Prediction in Metagenomic Libraries Using the Self Organising Map and High Performance Computing Techniques. In: Dubitzky, W., Schuster, A., Sloot, P.M.A., Schroeder, M., Romberg, M. (eds) Distributed, High-Performance and Grid Computing in Computational Biology. GCCB 2007. Lecture Notes in Computer Science(), vol 4360. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69968-2_8
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DOI: https://doi.org/10.1007/978-3-540-69968-2_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69841-8
Online ISBN: 978-3-540-69968-2
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