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Mathematical models for exploring different aspects of genotoxicity and carcinogenicity databases.
Author(s) -
Romualdo Benigni,
Alessandro Giuliani
Publication year - 1991
Publication title -
environmental health perspectives
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.257
H-Index - 282
eISSN - 1552-9924
pISSN - 0091-6765
DOI - 10.1289/ehp.919681
Subject(s) - computer science , genotoxicity , multivariate statistics , data science , obstacle , database , data mining , information retrieval , machine learning , chemistry , organic chemistry , toxicity , political science , law
One great obstacle to understanding and using the information contained in the genotoxicity and carcinogenicity databases is the very size of such databases. Their vastness makes them difficult to read; this leads to inadequate exploitation of the information, which becomes costly in terms of time, labor, and money. In its search for adequate approaches to the problem, the scientific community has, curiously, almost entirely neglected an existent series of very powerful methods of data analysis: the multivariate data analysis techniques. These methods were specifically designed for exploring large data sets. This paper presents the multivariate techniques and reports a number of applications to genotoxicity problems. These studies show how biology and mathematical modeling can be combined and how successful this combination is.

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