Mathematical models for exploring different aspects of genotoxicity and carcinogenicity databases.
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.
Benigni
R
R
Laboratory of Comparative Toxicology and Ecotoxicology, Istituto Superiore di Sanitá, Rome, Italy.
Giuliani
A
A
D000818:Animals; D016466:CHO Cells / Q000187:drug effects; D015197:Carcinogenicity Tests; D002869:Chromosome Aberrations; D016000:Cluster Analysis; D006224:Cricetinae; D016208:Databases, Factual; D007390:International Agencies; D007940:Leukemia L5178 / Q000235:genetics; D051379:Mice; D008962:Models, Theoretical; D015999:Multivariate Analysis; D009152:Mutagenicity Tests; D009316:National Institutes of Health (U.S.); D012486:Salmonella typhimurium / Q000187:drug effects; D012854:Sister Chromatid Exchange / Q000187:drug effects; D014116:Toxicology; D014481:United States