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Boolean implication networks derived from large scale, whole genome microarray datasets
We describe a method for extracting Boolean implications (if-then relationships) in very large amounts of gene expression microarray data. A meta-analysis of data from thousands of microarrays for humans, mice, and fruit flies finds millions of implication relationships between genes that would be m...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2760884/ https://www.ncbi.nlm.nih.gov/pubmed/18973690 http://dx.doi.org/10.1186/gb-2008-9-10-r157 |
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author | Sahoo, Debashis Dill, David L Gentles, Andrew J Tibshirani, Robert Plevritis, Sylvia K |
author_facet | Sahoo, Debashis Dill, David L Gentles, Andrew J Tibshirani, Robert Plevritis, Sylvia K |
author_sort | Sahoo, Debashis |
collection | PubMed |
description | We describe a method for extracting Boolean implications (if-then relationships) in very large amounts of gene expression microarray data. A meta-analysis of data from thousands of microarrays for humans, mice, and fruit flies finds millions of implication relationships between genes that would be missed by other methods. These relationships capture gender differences, tissue differences, development, and differentiation. New relationships are discovered that are preserved across all three species. |
format | Text |
id | pubmed-2760884 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-27608842009-10-13 Boolean implication networks derived from large scale, whole genome microarray datasets Sahoo, Debashis Dill, David L Gentles, Andrew J Tibshirani, Robert Plevritis, Sylvia K Genome Biol Method We describe a method for extracting Boolean implications (if-then relationships) in very large amounts of gene expression microarray data. A meta-analysis of data from thousands of microarrays for humans, mice, and fruit flies finds millions of implication relationships between genes that would be missed by other methods. These relationships capture gender differences, tissue differences, development, and differentiation. New relationships are discovered that are preserved across all three species. BioMed Central 2008 2008-10-30 /pmc/articles/PMC2760884/ /pubmed/18973690 http://dx.doi.org/10.1186/gb-2008-9-10-r157 Text en Copyright © 2008 Sahoo et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Method Sahoo, Debashis Dill, David L Gentles, Andrew J Tibshirani, Robert Plevritis, Sylvia K Boolean implication networks derived from large scale, whole genome microarray datasets |
title | Boolean implication networks derived from large scale, whole genome microarray datasets |
title_full | Boolean implication networks derived from large scale, whole genome microarray datasets |
title_fullStr | Boolean implication networks derived from large scale, whole genome microarray datasets |
title_full_unstemmed | Boolean implication networks derived from large scale, whole genome microarray datasets |
title_short | Boolean implication networks derived from large scale, whole genome microarray datasets |
title_sort | boolean implication networks derived from large scale, whole genome microarray datasets |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2760884/ https://www.ncbi.nlm.nih.gov/pubmed/18973690 http://dx.doi.org/10.1186/gb-2008-9-10-r157 |
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