Visualization Summit 2007: Ten research goals for 2010

At the first international Visualization Summit, more than 100 international researchers and practitioners defined and assessed nine original and important research goals in the context of Visualization Science, and proposed methods for achieving these goals by 2010. The synthesis of the whole event is presented in the 10th research goal. This article contributes a building block for systemizing visualization research by proposing mutually elaborated research goals with defined milestones. Such a consensus on where to go together is only one step toward establishing visualization science in the long-term perspective as a discipline with comparable relevance to chemistry, mathematics, language, or history. First, this article introduces the conference setting. Second, it describes the research goals and findings from the nine workshops. Third, a survey among 62 participants about the originality and importance of each research goal is presented and discussed. Finally, the article presents a synthesis of the nine research goals in the form of a 10th research goal, namely Visualizing Future Cities. The article is relevant for visualization researchers, trend scouts, research programme directors who define the topics that get funds. © 2007 Palgr aveMacmillan Ltd. All rights reserved.

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).