edward@mail.ece.tamu.edu.
The adipose tissue biopsy technique is an important caveat to consider when designing, interpreting, and, most important, comparing microarray experiments. These results will have crucial implications for the clinical and physiopathologic understanding of human obesity and therapeutic approaches.
The stress hormone epinephrine produces major physiological effects on skeletal muscle. Here we determined skeletal muscle mRNA expression profiles before and during a 6-h epinephrine infusion performed in nine young men. Stringent statistical analysis of data obtained using 43,000 cDNA element microarrays showed that 1206 and 474 genes were upand down-regulated, respectively. Microarray data were validated using reverse transcription quantitative PCR. Norepinephrine is the major neurotransmitter in the peripheral sympathetic nervous system, whereas epinephrine is the primary hormone secreted by the adrenal medulla in humans. As part of the response to stress, release of both the neurotransmitter and hormone may be stimulated. Their effect on target tissues is mediated by 6␣-and 3-adrenoceptor subtypes (1). The variety of receptors and signal transduction pathways combined with differential tissue distribution accounts for the diversity of biological responses. Epinephrine acts both on ␣-and -adrenoceptors. Intravenously it evokes an increase in blood pressure that is explained by a direct myocardial stimulation through -adrenoceptors and a vasoconstriction in many vascular beds through ␣-adrenoceptors. However, blood flow is markedly increased in skeletal muscle through the powerful  2 -adrenoceptor-mediated vasodilation. The hormone also influences a number of important metabolic processes (2). It decreases the uptake of glucose in peripheral tissues, partly through an inhibition of insulin secretion. It stimulates glycogenolysis in several organs and has a well-characterized effect on adipose tissue lipolysis that allows modulation of plasma free fatty acid levels (3). Epinephrine stimulates energy expenditure in humans. This effect is mediated by  1 -and  2 -adrenoceptors (4). The sympathetically mediated thermogenesis can be explained by a moderate increase in myocardial energy expenditure; an increase in adipose tissue lipolysis; and an increase in substrate oxidation, most notably in skeletal muscle. Catecholamines have also a profound effect on protein metabolism in skeletal muscle (5). Treatment with  2 -adrenergic agonists induces hypertrophy of skeletal muscle in livestock and humans due to an increased rate of protein synthesis and a reduced rate of protein breakdown.Skeletal muscle is equipped uniquely with the  2 -adrenoceptor that accounts for the effect of epinephrine on the
Classification in bioinformatics often suffers from small samples in conjunction with large numbers of features, which makes error estimation problematic. When a sample is small, there is insufficient data to split the sample and the same data are used for both classifier design and error estimation. Error estimation can suffer from high variance, bias, or both. The problem of choosing a suitable error estimator is exacerbated by the fact that estimation performance depends on the rule used to design the classifier, the feature-label distribution to which the classifier is to be applied, and the sample size. This paper reviews the performance of training-sample error estimators with respect to several criteria: estimation accuracy, variance, bias, correlation with the true error, regression on the true error, and accuracy in ranking feature sets. A number of error estimators are considered: resubstitution, leave-one-out cross-validation, 10-fold cross-validation, bolstered resubstitution, semi-bolstered resubstitution, .632 bootstrap, .632+ bootstrap, and optimal bootstrap. It illustrates these performance criteria for certain models and for two real data sets, referring to the literature for more extensive applications of these criteria. The results given in the present paper are consistent with those in the literature and lead to two conclusions: (1) much greater effort needs to be focused on error estimation, and (2) owing to the generally poor performance of error estimators on small samples, for a conclusion based on a small-sample error estimator to be considered valid, it should be supported by evidence that the estimator in question can be expected to perform sufficiently well under the circumstances to justify the conclusion.
This paper addresses the problem of improving accuracy in the machine-learning task of classification from microarray data. One of the known issues specifically related to microarray data is the large number of inputs (genes) versus the small number of available samples (conditions). A promising direction of research to decrease the generalization error of classification algorithms is to perform gene selection so as to identify those genes which are potentially most relevant for the classification. Classical feature selection methods are based on direct statistical methods. We present a reduction algorithm based on the notion of prototypegene. Each prototype represents a set of similar gene according to a given clustering method. We present experimental evidence of the usefulness of combining prototype-based feature selection with statistical gene selection methods for the task of classifying adenocarcinoma from gene expressions.
The aim of many microarray experiments is to build discriminatory diagnosis and prognosis models. Given the huge number of features and the small number of examples, model validity which refers to the precision of error estimation is a critical issue. Previous studies have addressed this issue via the deviation distribution (estimated error minus true error), in particular, the deterioration of cross-validation precision in high-dimensional settings where feature selection is used to mitigate the peaking phenomenon (overfitting). Because classifier design is based upon random samples, both the true and estimated errors are sample-dependent random variables, and one would expect a loss of precision if the estimated and true errors are not well correlated, so that natural questions arise as to the degree of correlation and the manner in which lack of correlation impacts error estimation. We demonstrate the effect of correlation on error precision via a decomposition of the variance of the deviation distribution, observe that the correlation is often severely decreased in high-dimensional settings, and show that the effect of high dimensionality on error estimation tends to result more from its decorrelating effects than from its impact on the variance of the estimated error. We consider the correlation between the true and estimated errors under different experimental conditions using both synthetic and real data, several feature-selection methods, different classification rules, and three error estimators commonly used (leave-one-out cross-validation, -fold cross-validation, and .632 bootstrap). Moreover, three scenarios are considered: (1) feature selection, (2) known-feature set, and (3) all features. Only the first is of practical interest; however, the other two are needed for comparison purposes. We will observe that the true and estimated errors tend to be much more correlated in the case of a known feature set than with either feature selection or using all features, with the better correlation between the latter two showing no general trend, but differing for different models.
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