MOTIVATION: Since DNA microarray experiments provide us with huge amount of gene expression data, they should be analyzed with statistical methods to extract the meanings of experimental results. Some dimensionality reduction methods such as Principal Component Analysis (PCA) are used to roughly visualize the distribution of high dimensional gene expression data. However, in the case of binary classification of gene expression data, PCA does not utilize class information when choosing axes. Thus clearly separable data in the original space may not be so in the reduced space used in PCA. RESULTS: For visualization and class prediction of gene expression data, we have developed a new SVM-based method called multidimensional SVMs, that generate multiple orthogonal axes. This method projects high dimensional data into lower dimensional space to exhibit properties of the data clearly and to visualize a distribution of the data roughly. Furthermore, the multiple axes can be used for class prediction. The basic properties of conventional SVMs are retained in our method: solutions of mathematical programming are sparse, and nonlinear classification is implemented implicitly through the use of kernel functions. The application of our method to the experimentally obtained gene expression datasets for patients' samples indicates that our algorithm is efficient and useful for visualization and class prediction. CONTACT: [email protected].

The current technology of parallel and distributed systems allows users to exploit a variety of resources across networks. However, the support provided is often insufficient for computational scientists to simulate complicated real-world scenarios in which different kinds of scientific applications need to be combined to perform high fidelity simulations. As a result, users waste a large amount of time and effort developing custom techniques for performing semantic-level communication between heterogeneous scientific simulations. This paper describes a new middleware system that provides high-level transparency in the form of agents that automatically transfer and transform data between simulations that use different mathematical and physical modeling approaches. Based on a specification that correlates different discrete points in finite difference method (FDM), finite element method (FEM) or particle simulations, the agents provide a variety of techniques for semantically transforming the values associated with correlated points and automatically determine to which processes the values must be transferred. To facilitate use and minimize impact on user programs, the agent system includes three types of library calls that manage task identification, register different kinds of discrete points and construct a correlation table according to the specification, and transfer messages that incorporate extraction and transformation of the values on the correlated points. Another library specially optimized for parallel simulations that use a SPMD (Single Program Multiple Data stream) structure is also offered to control communication through the agents. A prototype system has been developed on the Hitachi SR2201 parallel machine as well as workstation clusters, and applied to several example applications. These include an advanced device simulation that combines quantum transport simulation with electric potential simulation, and a simulation of thermal flow resulting from high-frequency device operation that hybridizes molecular dynamics simulation with macroscopic continuum simulation. These combinations can be efficiently realized using the small number of library calls within the agent system together with additional routines that change the data formats of discrete points. The time overhead of the agent calculations is shown experimentally to agree closely with the theoretically-predicted values modeled as a function of the number of discrete points and domain decomposition in parallel simulations. This value becomes insignificant compared with that of the simulation processes for heterogeneous large-scale simulations.