The NIEHS Predictive-Toxicology Evaluation Project: the need to distinguish informed uncertainty from ignorant equivocation.
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The NIEHS Predictive-Toxicology Evaluation Project: the need to distinguish informed uncertainty from ignorant equivocation.
stated that the Focus article did not "ade-quately explore the artist's or art faculty responsibility for learning to use art materials properly," and that Monona Rossol and I didn't believe that the hazard labeling regulations for art materials were adequate. Gottsegen also states that Rossol and I "place the blame for all this in the laps of the manufacturers of art materials or consulting to...
متن کاملThe NIEHS Predictive-Toxicology Evaluation Project.
The Predictive-Toxicology Evaluation (PTE) project conducts collaborative experiments that subject the performance of predictive-toxicology (PT) methods to rigorous, objective evaluation in a uniquely informative manner. Sponsored by the National Institute of Environmental Health Sciences, it takes advantage of the ongoing testing conducted by the U.S. National Toxicology Program (NTP) to estim...
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chapter i provides an overview of structural linguistics and touches upon the saussurean dichotomies with the final goal of exploring their relevance to the stylistic studies of literature. to provide evidence for the singificance of the study, chapter ii deals with the controversial issue of linguistics and literature, and presents opposing views which, at the same time, have been central to t...
15 صفحه اولThe Predictive Toxicology Evaluation Challenge
Can an AI program contribute to scientific discovery? An area where this gauntlet has been thrown is that of understanding the mechanisms of chemical carcinogenesis. One approach is to obtain Structure-Activity Relationships (SARs) relating molecular structure to cancerous activity. Vital to this are the rodent carcinogenicity tests conducted within the US National Toxicology Program by the Nat...
متن کاملStatistical Evaluation of The Predictive Toxicology Challenge
Motivation The development of in silico models to predict chemical carcinogenesis from molecular structure would help greatly to prevent environmentally caused cancers. The Predictive Toxicology Challenge (PTC) competition was organized to test the state-of-the-art in applying machine learning to form such predictive models. Results Fourteen machine learning groups generated 111 models. The use...
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ژورنال
عنوان ژورنال: Environmental Health Perspectives
سال: 1997
ISSN: 0091-6765,1552-9924
DOI: 10.1289/ehp.105-1470096