Millan K. Yeung

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We propose a scheme to reverse-engineer gene networks on a genome-wide scale using a relatively small amount of gene expression data from microarray experiments. Our method is based on the empirical observation that such networks are typically large and sparse. It uses singular value decomposition to construct a family of candidate solutions and then uses(More)
While the fundamental building blocks of biology are being tabulated by the various genome projects, microarray technology is setting the stage for the task of deducing the connectivity of large-scale gene networks. We show how the perturbation of carefully chosen genes in a microarray experiment can be used in conjunction with a reverse engineering(More)
Arc splines are important in automatically controlled complex curve cutting process. However, the problem of how to determine the parameter of arcs according to desired curve fitting accuracy has not been completely solved. This paper presents a new algorithm for finding arbitrarily close bi-arc splines. It is based on research on the characteristics of(More)
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