Computers and statistics

Microarray experiments generate large and complex data sets that constitute e.g. lists of spot intensities and intensity ratios. Basically, the data obtained from microarray experiments provide information on the relative expression of genes corresponding to the mRNA sample of interest. Computational and statistical tools are required to analyze the large amount of data to address biological questions. To this end, a variety of analytical platforms are available, either free on the Web or via purchase of a commercially available product. 

A natural extension of analytical automation is some means of data processing all the results that are generated. This usually takes the form of a central computer which accepts information from different analysers for presentation in a useful manner. The identification of a sample and the tests performed are typed in using a keyboard and the computer collates all the data on each sample. As well as collecting information, computing and statistically assessing results, an important facility of the computer lies in its ability to store information for future recall via a visual display unit. 

The latter danger is, of course, potentially present any time any data interpretation is attempted, particularly if nature is assumed always to follow Eq. (1). The only course of action is to attempt to include as much theory in the model as possible, and to confirm any substantial extrapolation by experiment. It is erroneous, however, to presume that kinetic data will always be so imprecise as to be misleading. The use of computers and statistical analyses for any linear or nonlinear reaction rate model allows rather definite statements about the amount of information obtained from a set of data. Hence, although imprecision in analyses may exist, it need not go unrecognized and perhaps become misleading. 

As analysts become more aware of the fact that automation offers a variety of tools, such as robotics, computing, and statistics, to solve analytical problems, then future progress will be far quicker, allowing the installation of more successful and reliable automatic systems. 

DDS05c] M. Deza, M. Dutour, and M. I. Shtogrin, Elementary elliptic (R,q)-poIycycles, chapter in Analytic, Computational and Statistical Approaches for Network Analysis, ed. by M. Dehmer and F. Emmert-Streib, Wiley, to appear. 

In order to correctly design analytical procedures used for the detection of food allergens, it is necessary to have basic knowledge of food product chemistry to know how to collect, prepare, and store food samples to be able to fragment, mix, disintegrate, and extract samples to know (or be able to find quickly) relevant food quality standards and admissible contents of particular food ingredients and finally to understand precision of determinations, their sensitivity, and detection threshold levels, reproducibility, and errors of determination methods. In addition, it is essential to be able to gather the results of assays, process them with the aid of a computer and statistical methods, and to present the analytically derived data. 

The author would like to thank Dr. J. R. Green of the Department of Computational and Statistical Science, The University of Liverpool, for helpful discussion. 

The high-throughput data cluster appears to be related to using computational and statistical techniques to separate useful data signals from large amounts of irrelevant noise (e.g., see [48]). This important endeavor is just in its infancy. 

Computational and Statistical Methodologies for ORFeome Primary Structure Analysis... 

A somewhat simpler, but not fundamental, approach is given in Ref. 376. The mass of data for various types of roughness (Fig. 11.8) is now so large that straightforward power-law correlations for Nusselt number and friction factor can be generated using large computers and statistical analysis software. The power-law correlations developed in this manner are... 

Zavolan M, Schonbach C 2006 The contribution of alternative transcription and alternative splicing to the complexity of mammalian transcriptomes. In Zhang W, Shmulevich I (eds) Computational and statistical approaches to genomics, 2nd edn. Springer Science + Business Media Inc. New York, p 351—380... 

Fiirst Sandor utca 5 H-1136 Budapest, Hungary Tel. 36-1-112-4874, fax 36-1-132-2574 Prediction of metabolic products based on a library of known transformations. HARZARDEXPERT for prediction of toxicity. MOLIDEA for molecular modeling, molecular mechanics CNDO/2 and CNDO/S calculations. DRUGIDEA for QSAR. LABSWARE for general computations and statistics. PRO-LOGP for lipophilicity calculation using R. F. Rekker method. PC. 

Department of Mathematics, Department of Biochemistry and Molecular Biology, Michigan State University, Ml 48824, USA Computational and Statistical Analytics Division,... 

DAB reception begins in a similar manner as is used in virtually aU radio receivers. A receiving antenna feeds an appropriate stage of RF selectivity and amphfication, from which a sample of the coded DAB signal is derived. This signal then drives a channel decoder, which reconstructs the audio and auxiliary data streams. To accomplish this task, the channel decoder must demodulate and de-interleave the data contained on the RF carrier and then apply appropriate computational and statistical error correction functions. 

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