Geometric optimization methods for the analysis of gene expression data

DNA microarrays provide such a huge amount of data that unsupervised methods are required to reduce the dimension of the data set and to extract meaningful biological information. This work shows that Independent Component Analysis (ICA) is a promising approach for the analysis of genome-wide transcriptomic data. The paper first presents an overview of the most popular algorithms to perform ICA. These algorithms are then applied on a microarray breast-cancer data set. Some issues about the application of ICA and the evaluation of biological relevance of the results are discussed. This study indicates that ICA significantly outperforms Principal Component Analysis (PCA).

The analysis of stochastic EMC problems using the concept of diffuse field reciprocity

There has been much progress in recent years in the analysis of complex random vibro-acoustic systems, and general analysis methods have been developed which are based on the properties of diffuse wave fields. It is shown in the present paper that such methods can also be applied to high frequency EMC problems, avoiding the need for costly full wave solutions to Maxwell's equations in complex cavities. The theory behind the approach is outlined and then applied to the relatively simple case of a wiring system which is subject to reverberant electromagnetic wave excitation. © 2011 IEEE.