Artificial neural network study on organ-targeting peptides.
ABSTRACT We report a new approach to studying organ targeting of peptides on the basis of peptide sequence information. The positive control data sets consist of organ-targeting peptide sequences identified by the peroral phage-display technique for four organs, and the negative control data are prepared from random sequences. The capacity of our models to make appropriate predictions is validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC) curve (the ROC score). VHSE descriptor produces statistically significant training models and the models with simple neural network architectures show slightly greater predictive power than those with complex ones. The training and test set statistics indicate that our models could discriminate between organ-targeting and random sequences. We anticipate that our models will be applicable to the selection of organ-targeting peptides for generating peptide drugs or peptidomimetics.
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ABSTRACT: A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.Radiology 05/1982; 143(1):29-36. · 6.34 Impact Factor
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ABSTRACT: Vascular beds are known to differ in structure and metabolic function, but less is known about their molecular diversity. We have studied organ-specific molecular differences of the endothelium in various tissues by using in vivo screening of peptide libraries expressed on the surface of a bacteriophage. We report here that targeting of a large number of tissues with this method yielded, in each case, phage that homed selectively to the targeted organ. Different peptide motifs were recovered from each of these tissues. The enrichment in homing to the target organs relative to an unselected phage was 3-35-fold. Peptide sequences that conferred selective phage homing to the vasculature of lung, skin, and pancreas were characterized in detail. Immunohistochemistry showed that the phage localized in the blood vessels of their target organ. When tested, the phage homing was blocked in the presence of the cognate peptide. By targeting several tissues and by showing that specific homing could be achieved in each case, we provide evidence that organ- and tissue-specific molecular heterogeneity of the vasculature is a general, perhaps even universal, phenomenon. Our results also show that these molecular differences can serve as molecular addresses.Journal of Clinical Investigation 08/1998; 102(2):430-7. · 12.81 Impact Factor
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ABSTRACT: To better evaluate, in the context of QSAR studies, new validation techniques such as bootstrapping and crossvalidation and the new analytic technique of partial least squares (PLS), seventeen QSAR results taken from nine recent publications were reexamined using these techniques. The results indicate that bootstrapping and crossvalidation are more powerful indicators of possible chance correlation than are the classical tests based on assumed normal independent distribution of variables. Although PLS will not detect all correlations existing within a set of data, its conservative behavior is particularly valuable when the candidate physicochemical descriptors are numerous and non-orthogonal.Quantitative Structure-Activity Relationships 09/2006; 7(1):18 - 25.