By Christoph Helma (Editor)
A entire evaluate of ideas and structures at present used in predictive toxicology, this reference provides an in-depth survey of techniques to signify chemical buildings and organic systems—covering prediction tools and algorithms, resources of fine quality toxicity facts, crucial advertisement and noncommercial predictive toxicology courses, and complex applied sciences in computational chemistry and biology, statistics, and information mining.
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The course of a dynamic process, more complex designs are required. , distinct compounds) are compared to a similar reference condition. The reference condition can be artificial and does not need to be biologically significant. Its main purpose is to have a common baseline to facilitate mutual comparison between samples. Every reference design results in a relatively higher number of replicate measurements of the condition (reference) in which one is not primarily interested than of the condition of interest (test condition).
The results of this fit can be used to simultaneously linearize and normalize the data (Fig. 5). The array by array procedure uses the global properties of all genes on the array to calculate the normalization factor. Other approaches have been described that subdivide an array into, for instance, individual print tip groups, which are normalized separately (32). Theoretically, these approaches perform better than the array by array approach in removing position-dependent ‘‘within array’’ variations.
Using more complex designs, one can try to reconstruct the regulation network that generates a certain behavior. Dynamic changes in expression can be monitored as function of time. For such a dynamic experiment, the main purpose is to find genes that behave similarly during the time course, where often an appropriate definition of similarity is one of the problems. Such coexpressed genes are identified by cluster analysis (Sec. 2). , the effect induced by distinct xenobiotics). One is interested not only in finding coexpressed genes, but also in knowing the experimental conditions that group together based on their experiment profiles.