Krylov subspace algorithms for computing GeneRank for the analysis of microarray data mining.
GeneRank is a new engine technology for the analysis of microarray experiments. It combines gene expression information with a network structure derived from gene notations or expression profile correlations. Using matrix decomposition techniques, we first give a matrix analysis of the GeneRank model. We reformulate the GeneRank vector as a linear combination of three parts in the general case when the matrix in question is non-diagonalizable. We then propose two Krylov subspace methods for computing GeneRank. Numerical experiments show that, when the GeneRank problem is very large, the new algorithms are appropriate choices.
Wu
Gang
G
School of Mathematical Sciences, Xuzhou Normal University, Xuzhou, Jiangsu, PR China.
Zhang
Ying
Y
Wei
Yimin
Y
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D008957:Models, Genetic; D009716:Numerical Analysis, Computer-Assisted; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*