Prediction of the Carcinogenicity of a Second Group of Organic Chemicals Undergoing Carcinogenicity Testing

Zhang, Y. P.; Sussman, N.; Macina, O. T.; Rosenkranz, Herbert S.; Klopman, G.

doi:10.2307/3433029

Cited by 20 publications

(17 citation statements)

References 2 publications

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“…Bayes' theorem [22] was used to generate a single overall prediction of activity by combining the four SAR submodels [23]. A prior probability of 0.1 was chosen for the initial probability.…”

Section: Qsars For Predicting Respiratory Sensitizationmentioning

confidence: 99%

Quantitative structure-activity relationships for predicting skin and respiratory sensitization

Rodford¹,

Patlewicz

Walker

et al. 2003

Environmental Toxicology and Chemistry

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Abstract-Quantitative structure-activity relationships (QSARs) for predicting skin and respiratory sensitization are reviewed. Overall, progress has been hampered by the sparseness of good quality experimental data, a fact that makes it difficult, at this time, to recommend one or two QSARs for predicting skin and respiratory sensitization. Creation of appropriate data sets for uninvestigated classes of chemicals by experimentation should facilitate the development of more robust QSARs for predicting skin and respiratory sensitization. Such QSARs will be valuable in the evaluation of identifiable toxic hazards where dose responses are relevant, as is the case for skin and respiratory sensitization.

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Section: Qsars For Predicting Respiratory Sensitizationmentioning

confidence: 99%

Quantitative structure-activity relationships for predicting skin and respiratory sensitization

Rodford¹,

Patlewicz

Walker

et al. 2003

Environmental Toxicology and Chemistry

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“…The decision-tree approach proposed in this article is a sequential test strategy which consists of a sequence of decision rules based on the data which have been collected or will become available. The sequential test strategy is more useful in the development of a predictive system (e.g., Zhang et al, 1996). In addition to estimating the likelihood of being positive based on the existing data, the decision-tree approach explores the need for conducting more tests.…”

Section: Discussionmentioning

confidence: 99%

“…The common approach to predictions or classifications is based on point estimates using, say, 0.50 as a cut-off probability (e.g., Katz and Foxman, 1993;Zhang et al, 1996). In this article we propose using statistical confidence limits, an upper bound and a lower bound for cut-off points.…”

Section: Discussionmentioning

confidence: 99%

A Decision-Tree Strategy for Combining Diagnostic Tests for Prediction

Chen

Kodell

1999

Biom. J.

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In the context of medical screening, various diagnostic tests have been developed for determining whether a disease is present in an individual. Similarly, in the context of toxicological screening, a variety of short‐term assays have been developed to predict whether a chemical would be carcinogenic if tested in a long‐term bioassay. In both contexts, it is a challenge to combine the results of several predictive tests in a way that improves on the predictivity of the individual tests. Increases in positive predictivity can be accompanied by decreases in negative predictivity, and vice versa. This article presents a decision‐tree classification model for combining results from several independent short‐term or diagnostic tests to quantify the likelihood of a true positive result (patient has disease, or chemical is carcinogenic). The decision‐tree strategy determines the most advantageous sequence for conducting the predictive tests. The classification model is based on statistical confidence limits on the predictive probability of disease (carcinogenicity) rather than on the central estimate of the predictive probability. This model is applied to the assessment of the abilities of four short‐term tests in the prediction of chemical carcinogenicity under the assumption of independence among the four tests, and is used to demonstrate a testing strategy for the application of three pancreatic cancer diagnostic tests.

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“…Active and inactive chemicals were simulated only in terms of a model dependent variable. In other words, it is assumed that either an SAR or OSAR computer program, such as CASE/MultiCASE [5,6], a neural network [7] or discriminant analysis, has already calculated the dependent variable for known numbers of actives N A and inactives N I in a data base. This was accomplished by contriving idealized distributions for``actives'' and``inactives.''…”

Section: Methodsmentioning

confidence: 99%

A Simple Inter-Class Distance Parameter for Predictive SAR/QSAR Models

Claycamp

Sussman

1999

Quant. Struct.-Act. Relat.

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The objective of many SAR or QSAR experiments is to develop statistical classi®cation models that can separate chemicals into classes of``active'' or``inactive'' under a toxicological endpoint. Several existing statistical methods are often used to provide either quantitative or qualitative measures of the distance or separation between the active and inactive classes, including sensitivity, accuracy and the receiver-operator characteristic (ROC) curve. The present study proposes a simple``distance parameter'' as a measure of the relative separation between classes of``active'' and`i nactive'' chemicals. The distance parameter can be used alone or with existing statistical measures for both the optimization of a single model with respect to maximizing sensitivity and speci®city, and also in the selection of the best model from among alternative models.

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Prediction of the Carcinogenicity of a Second Group of Organic Chemicals Undergoing Carcinogenicity Testing

Cited by 20 publications

References 2 publications

Quantitative structure-activity relationships for predicting skin and respiratory sensitization

Quantitative structure-activity relationships for predicting skin and respiratory sensitization

A Decision-Tree Strategy for Combining Diagnostic Tests for Prediction

A Simple Inter-Class Distance Parameter for Predictive SAR/QSAR Models

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