The ranking of genes plays an important role in biomedical research. The GeneRank method of Morrison et al. [BMC Bioinformatics, 6:233 (2005)] ranks genes based on the results of microarray experiments combined with gene expression information, for example from gene annotations. The algorithm is a variant of the well known PageRank iteration, and can be formulated as the solution of a large, sparse linear system. Here we show that classical Chebyshev semi-iteration can considerably speed up the convergence of GeneRank, outperforming other acceleration schemes such as conjugate gradients. Copyright © 2013, Kent State University.
Chebyshev acceleration of the GeneRank algorithm
Benzi, Michele;
2013
Abstract
The ranking of genes plays an important role in biomedical research. The GeneRank method of Morrison et al. [BMC Bioinformatics, 6:233 (2005)] ranks genes based on the results of microarray experiments combined with gene expression information, for example from gene annotations. The algorithm is a variant of the well known PageRank iteration, and can be formulated as the solution of a large, sparse linear system. Here we show that classical Chebyshev semi-iteration can considerably speed up the convergence of GeneRank, outperforming other acceleration schemes such as conjugate gradients. Copyright © 2013, Kent State University.File in questo prodotto:
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