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Applications of computers to toxicological research

Authors: George W. A. Milne; Shaomeng Wang;

Applications of computers to toxicological research

Abstract

Computers are used in toxicology in two ways. They are able to manage and manipulate large amounts of data, and it is because of this that they are used quite commonly to search toxicity databases. The mechanical ability of computers has led a number of organizations to pursue their use in regulatory compliance. The cost-benefit aspect of this issue being what it is, much more effort can be expected in this area. The other major use of computers has been to support efforts to predict or estimate toxicity properties. This task has proven to be very difficult, as was expected, and progress has been mixed. Developers of systems, testing their own development, report impressive accuracy, as has been seen. The "real world" view is less felicitous. In a highly publicized, head-to-head test of some of the computer methods against human experts, accurate prediction of carcinogenicity by computer was achieved for 49-59% of the compounds, depending upon the method used. The humans, on the other hand, scored between 65% and 84%. A conclusion that could be drawn from this experiment is that with compounds which "obviously" are or are not carcinogenic, both computers and humans score well. Once obviousness recedes however, both are at a disadvantage, but humans can improvise more effectively. As research continues, the computer methods will develop better learning sets, and so there will be incremental improvements in their performance.(ABSTRACT TRUNCATED AT 250 WORDS)

Keywords

Databases, Factual, Computers, Toxicology

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Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Average
Top 10%
Average
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