Volume 40, pp. 311-320, 2013.

Chebyshev acceleration of the GeneRank algorithm

Michele Benzi and Verena Kuhlemann

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.

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Key words

GeneRank, computational genomics, Chebyshev semi-iteration, polynomials of best uniform approximation, conjugate gradients

AMS subject classifications

65F10, 65F50; 9208, 92D20

ETNA articles which cite this article

Vol. 41 (2014), pp. 179-189 Davod Khojasteh Salkuyeh, Vahid Edalatpour, and Davod Hezari: Polynomial Preconditioning for the GeneRank Problem
Vol. 45 (2016), pp. 146-159 Florian Gossler and Reinhard Nabben: On AMG methods with F-smoothing based on Chebyshev polynomials and their relation to AMGr

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