Simplivariate Models: Uncovering the Underlying Biology in Functional Genomics Data

One of the first steps in analyzing high-dimensional functional genomics data is an exploratory analysis of such data. Cluster Analysis and Principal Component Analysis are then usually the method of choice. Despite their versatility they also have a severe drawback: they do not always generate simple and interpretable solutions. On the basis of the observation that functional genomics data often contain both informative and non-informative variation, we propose a method that finds sets of variables containing informative variation. This informative variation is subsequently expressed in easily interpretable simplivariate components

CURVE-FITTING USING NATURAL COMPUTATION

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Quality criteria of genetic algorithms for structure optimization

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Pool Heuristics in Evolutionary Database Optimization

This paper describes several new results of the project C2I at the University of Nijmegen, The Netherlands. The focus of C2I is the transformation of data models from the conceptual to the internal level. Several aspects are involved, such as data structures, operations, populations (values) and integrity constraints. Special emphasis is given to optimization of data structures with respect to the trade-off between response time and storage space. New results include PHSL, an abstract Pool Heuristic Specification Language for expressing the basic actions in advanced (evolutionary) optimization strategies. The paper also introduces an instrument for embedding conventional physical database design algorithms (index selection) within these advanced strategies. Furthermore, the applicability of the approach to traditional Codasyl network models is illustrated (it has already been shown that the approach is applicable to relational and nested-relational models). 1 Introduction During the ..

A two-step approach toward model-free X-ray fluorescence analysis of layered materials

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