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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,...

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Detalles Bibliográficos
Autores principales: Benigni, R, Giuliani, A
Formato: Texto
Lenguaje:English
Publicado: 1991
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1568235/
https://www.ncbi.nlm.nih.gov/pubmed/1820283
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author Benigni, R
Giuliani, A
author_facet Benigni, R
Giuliani, A
author_sort Benigni, R
collection PubMed
description 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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spelling pubmed-15682352006-09-18 Mathematical models for exploring different aspects of genotoxicity and carcinogenicity databases. Benigni, R Giuliani, A Environ Health Perspect Research Article 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. 1991-12 /pmc/articles/PMC1568235/ /pubmed/1820283 Text en
spellingShingle Research Article
Benigni, R
Giuliani, A
Mathematical models for exploring different aspects of genotoxicity and carcinogenicity databases.
title Mathematical models for exploring different aspects of genotoxicity and carcinogenicity databases.
title_full Mathematical models for exploring different aspects of genotoxicity and carcinogenicity databases.
title_fullStr Mathematical models for exploring different aspects of genotoxicity and carcinogenicity databases.
title_full_unstemmed Mathematical models for exploring different aspects of genotoxicity and carcinogenicity databases.
title_short Mathematical models for exploring different aspects of genotoxicity and carcinogenicity databases.
title_sort mathematical models for exploring different aspects of genotoxicity and carcinogenicity databases.
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1568235/
https://www.ncbi.nlm.nih.gov/pubmed/1820283
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