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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...

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Detalles Bibliográficos
Autores principales: Sahoo, Debashis, Dill, David L, Gentles, Andrew J, Tibshirani, Robert, Plevritis, Sylvia K
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
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.
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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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