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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,...
Autores principales: | , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
1991
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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. |
format | Text |
id | pubmed-1568235 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 1991 |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT benignir mathematicalmodelsforexploringdifferentaspectsofgenotoxicityandcarcinogenicitydatabases AT giuliania mathematicalmodelsforexploringdifferentaspectsofgenotoxicityandcarcinogenicitydatabases |