From co-expression to co-regulation: how many microarray experiments do we need?
about
Pathway connectivity and signaling coordination in the yeast stress-activated signaling network.MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequencesStatistical signals in bioinformatics.Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological dataIdentifying subspace gene clusters from microarray data using low-rank representation.CLEAN: CLustering Enrichment ANalysis.DGEclust: differential expression analysis of clustered count data.Cautions about the reliability of pairwise gene correlations based on expression data.CoXpress: differential co-expression in gene expression data.Reproducible clusters from microarray research: whither?Microarray data analysis: from disarray to consolidation and consensus.A robust measure of correlation between two genes on a microarray.Meta-coexpression conservation analysis of microarray data: a "subset" approach provides insight into brain-derived neurotrophic factor regulation.Reconstruction of gene regulatory modules in cancer cell cycle by multi-source data integration.Identification of cis-regulatory elements in the mammalian genome: the cREMaG databaseIdentifying functional relationships within sets of co-expressed genes by combining upstream regulatory motif analysis and gene expression information.Immobilization of Escherichia coli RNA polymerase and location of binding sites by use of chromatin immunoprecipitation and microarrays.Construction of regulatory networks using expression time-series data of a genotyped population.Dynamic modularity of host protein interaction networks in Salmonella Typhi infection.Network-based differential gene expression analysis suggests cell cycle related genes regulated by E2F1 underlie the molecular difference between smoker and non-smoker lung adenocarcinoma.Bottom-up GGM algorithm for constructing multilayered hierarchical gene regulatory networks that govern biological pathways or processes.Approaches for extracting practical information from gene co-expression networks in plant biology.Tools for interpreting large-scale protein profiling in microbiology.Genes regulated by caloric restriction have unique roles within transcriptional networks.Relationship between gene co-expression and sharing of transcription factor binding sites in Drosophila melanogaster.Unraveling transcriptional control in Arabidopsis using cis-regulatory elements and coexpression networks.Wigwams: identifying gene modules co-regulated across multiple biological conditions.Time-resolved metabolomics reveals metabolic modulation in rice foliage.Sequencing of bulks of segregants allows dissection of genetic control of amylose content in rice.Stress-induced co-expression of alternative respiratory chain components in Arabidopsis thaliana.Enhancing biological relevance of a weighted gene co-expression network for functional module identification.Distinct regulatory elements mediate similar expression patterns in the excretory cell of Caenorhabditis elegans.TGMI: an efficient algorithm for identifying pathway regulators through evaluation of triple-gene mutual interaction.
P2860
Q27937397-3F09E843-53BE-4563-8E10-5AD76D07AE5AQ28710347-C4ADF8B7-FBAB-4456-8655-C2AFAEAF5A45Q28769392-BB1A5E12-9477-4B58-8EC1-9974E649D8ABQ30000991-3FD76427-E5BF-4D46-A4A9-BFBBAAE6730FQ30606803-F22BB11A-C294-4564-A6E7-E88ACBF7ED7AQ30871275-B307C6F9-10BE-404C-A3D8-A98428F884EEQ30927177-D2529421-868C-4634-B1DC-0D8B38BC4222Q30979865-9553EFA4-11FA-421B-AEB6-5ABF63593F78Q31078972-18CD7523-2B4D-47EB-A731-441FC799D37BQ33219773-BCA0BB8F-AF83-4DD7-9852-A87FE7A664B8Q33230059-F80F0782-1276-4199-9C38-989DF86E4A3FQ33288895-EEBED015-B77F-4C82-AAC1-89383CFFC05DQ33500860-B28CC079-98F3-443D-8551-3AF3D8EB1BD0Q33565567-758B19EE-2D8E-4511-AEA1-6B315D66A75DQ33687266-FFFFB847-A51C-4028-9871-A654AF66B7E9Q33737067-46BEF09E-BD3A-4167-9DC3-2F0F410D9D8FQ33937839-E673F00E-D675-4704-9A58-DE5160BB764EQ34074655-B30A518B-7025-4BFC-9943-0BEC1C4B7EEBQ34075421-3C55FEF3-2169-4A70-B512-1F3D5E5A11E7Q35068236-3B66D7E5-47E6-4088-A2B4-688CFE5BBE84Q35963301-D6E8D191-104A-4125-884D-F25D078B8F80Q36717674-F263B435-BBDC-48DB-8F10-77C243E582BDQ37140293-63A479D4-3BA9-4857-A633-C24174E12802Q37207519-F65CC1F6-7874-433C-95A8-15D881412D9EQ37364201-2B5BEB91-D724-4F82-8994-69BBCCC6918DQ38510491-E9BFA52E-C44C-4572-8693-DEF2E38034C5Q38534062-11CE5D04-3F81-466E-8449-6025EF3CDF25Q39660514-A0DDC5E7-BE44-40D0-AE54-4AAC2FC8FB18Q46522559-9E062216-195D-4D6B-8531-98A11B1601EFQ47761339-4D7B9DA7-F14D-4481-BE0F-E2C5E75C079CQ50312713-520EDCBC-E8BE-4728-8042-51EDF07992E5Q50754851-D9C95047-2A5E-49BA-A230-DCB8778F78BEQ52630976-34CDC352-8A5C-4206-8662-25EEFA350027
P2860
From co-expression to co-regulation: how many microarray experiments do we need?
description
2004 nî lūn-bûn
@nan
2004 թուականին հրատարակուած գիտական յօդուած
@hyw
2004 թվականին հրատարակված գիտական հոդված
@hy
2004年の論文
@ja
2004年論文
@yue
2004年論文
@zh-hant
2004年論文
@zh-hk
2004年論文
@zh-mo
2004年論文
@zh-tw
2004年论文
@wuu
name
From co-expression to co-regulation: how many microarray experiments do we need?
@ast
From co-expression to co-regulation: how many microarray experiments do we need?
@en
From co-expression to co-regulation: how many microarray experiments do we need?
@nl
type
label
From co-expression to co-regulation: how many microarray experiments do we need?
@ast
From co-expression to co-regulation: how many microarray experiments do we need?
@en
From co-expression to co-regulation: how many microarray experiments do we need?
@nl
prefLabel
From co-expression to co-regulation: how many microarray experiments do we need?
@ast
From co-expression to co-regulation: how many microarray experiments do we need?
@en
From co-expression to co-regulation: how many microarray experiments do we need?
@nl
P2860
P356
P1433
P1476
From co-expression to co-regulation: how many microarray experiments do we need?
@en
P2093
Mario Medvedovic
P2860
P2888
P356
10.1186/GB-2004-5-7-R48
P407
P577
2004-01-01T00:00:00Z
P5875
P6179
1007246950