about
MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic dataGAGE: generally applicable gene set enrichment for pathway analysisSelf-contained gene-set analysis of expression data: an evaluation of existing and novel methodsHarnessing the complexity of gene expression data from cancer: from single gene to structural pathway methodsGene set based integrated data analysis reveals phenotypic differences in a brain cancer model.Analyzing LC/MS metabolic profiling data in the context of existing metabolic networks.Evaluating gene set enrichment analysis via a hybrid data model.Identifying gene interaction enrichment for gene expression dataInferring pathway dysregulation in cancers from multiple types of omic data.Gene set analysis exploiting the topology of a pathway.Gene set analysis approaches for RNA-seq data: performance evaluation and application guidelineMicroarray-based gene set analysis: a comparison of current methodsA general modular framework for gene set enrichment analysis.A biological evaluation of six gene set analysis methods for identification of differentially expressed pathways in microarray dataCross species analysis of microarray expression dataMethods for interpreting lists of affected genes obtained in a DNA microarray experimentQuantitative comparison of microarray experiments with published leukemia related gene expression signatures.Testing for mean and correlation changes in microarray experiments: an application for pathway analysis.Functional analysis: evaluation of response intensities--tailoring ANOVA for lists of expression subsetsInvestigating the effect of paralogs on microarray gene-set analysis.A knowledge-based T2-statistic to perform pathway analysis for quantitative proteomic data.Gene set analysis methods: statistical models and methodological differences.A comparative study on gene-set analysis methods for assessing differential expression associated with the survival phenotypeAverage rank-based score to measure deregulation of molecular pathway gene setsFrom hybridization theory to microarray data analysis: performance evaluation.Uniform approximation is more appropriate for Wilcoxon Rank-Sum Test in gene set analysis.Assessment method for a power analysis to identify differentially expressed pathways.Linear combination test for gene set analysis of a continuous phenotypetimeClip: pathway analysis for time course data without replicatesAdaptive elastic-net sparse principal component analysis for pathway association testing.Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets.Potential tumorigenic programs associated with TP53 mutation status reveal role of VEGF pathwayPathNet: a tool for pathway analysis using topological informationGene expression patterns of sulfur starvation in Synechocystis sp. PCC 6803.Empirical pathway analysis, without permutation.Gene-set analysis and reduction.Choosing the right path: enhancement of biologically relevant sets of genes or proteins using pathway structureError control variability in pathway-based microarray analysis.Association between a prognostic gene signature and functional gene sets.A hypothesis test for equality of bayesian network models.
P2860
Q24621590-089F56E6-03A9-43CB-92E1-79BD9E7456E8Q24645628-2B46D635-6071-4545-B23C-094F3E29AFA3Q28475530-CFB960DE-C0C1-4833-A1AD-2257D1EF3C81Q30580995-FA5638F2-EB77-4F96-A027-E2E77555B0D9Q30656585-63D9F87D-2D29-4708-BC12-A19BAB3E0D63Q30665314-36659C2C-2959-45FD-A323-A7E642E4AA69Q30761703-5F533F6A-A980-4101-B4D9-5D2D652E848DQ30942442-52C35A9B-CC89-4DF7-92EE-CFE9F16B08ADQ30980125-6998CA12-67A3-4A66-80F7-7AE84C1D87B9Q30988680-2EC4E434-2640-42D2-8655-74E9825E1B78Q30991698-173C147E-480E-4D17-951D-4C1909A2565BQ33387712-410098E0-DD46-4373-AD4E-2FF9B20D4A42Q33405451-54F408BA-B1E1-4AB3-9F24-A38C872F4D68Q33414665-D2AFA354-0170-42FA-8CC3-28B0CB0CE8B6Q33428459-90F98E7F-51A6-4EEE-AABB-0C5B92330EB3Q33483788-1D11C1D2-2793-45BB-8E21-C207C2A77912Q33518038-80F878FB-618B-4FDD-BA16-815A1AF73D2CQ33527110-F5880DC9-9DC6-4B41-ABDA-32C32249506FQ33717474-0780B86B-2E0C-44F8-93C9-41F9F807DF8BQ33801632-D228C9FF-D5FE-4BCA-A033-A098770A98EEQ33860424-87FD47B5-8984-4F71-BEFC-9B3BD6E37F51Q33920272-4D939ADF-AC5B-4415-8312-3A31C3FB5D86Q34029377-B7D15973-C275-4B2D-95F2-119705A7DA0BQ34077968-49AD11E2-6B9A-498C-88AF-2C3D4CE8CBD5Q34089747-3039BA14-1716-4C49-BBFE-77EA0031FF40Q34162870-BF2D3BDB-F98F-47B2-BBCE-33A892047B8BQ34281947-45643F7D-5504-49CD-957E-4E16952B3B67Q34787731-A012A4FE-B360-418E-87F3-06A98AB2514CQ35215955-EB19B61B-5CAC-459D-8642-3EB8594CAC24Q35547962-2F09111C-B2AD-4B18-8416-91FFCF159799Q35694419-026564CA-B9BF-4124-A76C-186C570FCE07Q36385696-66ED5A9D-99CA-4464-AC02-0D9B89BC79ECQ36585204-872F4F87-32F2-498F-A5C4-5C0245C1B496Q36800864-BF0FD86B-580B-46BD-992C-A770C81E67B7Q36916419-EC44DB95-7F99-4252-9D8F-38225BC8ECB3Q37089701-F75CB6DB-7F36-419F-8FB1-391D25CEF575Q37208723-385786AA-590C-45DB-8958-1E0A6BE887EFQ37324771-0754D9CC-5111-468D-B30B-AE91953C4C56Q37328555-DB33B23C-5833-4113-AFB7-BB7E708C87F5Q38377315-C55FCAF6-3DE1-41AB-94A7-0BADFBA9F281
P2860
description
2007 nî lūn-bûn
@nan
2007 թուականի Նոյեմբերին հրատարակուած գիտական յօդուած
@hyw
2007 թվականի նոյեմբերին հրատարակված գիտական հոդված
@hy
2007年の論文
@ja
2007年論文
@yue
2007年論文
@zh-hant
2007年論文
@zh-hk
2007年論文
@zh-mo
2007年論文
@zh-tw
2007年论文
@wuu
name
Comparative evaluation of gene-set analysis methods
@ast
Comparative evaluation of gene-set analysis methods
@en
Comparative evaluation of gene-set analysis methods.
@nl
type
label
Comparative evaluation of gene-set analysis methods
@ast
Comparative evaluation of gene-set analysis methods
@en
Comparative evaluation of gene-set analysis methods.
@nl
prefLabel
Comparative evaluation of gene-set analysis methods
@ast
Comparative evaluation of gene-set analysis methods
@en
Comparative evaluation of gene-set analysis methods.
@nl
P2093
P2860
P356
P1433
P1476
Comparative evaluation of gene-set analysis methods
@en
P2093
Adeniyi J Adewale
Irina Dinu
Yutaka Yasui
P2860
P2888
P356
10.1186/1471-2105-8-431
P50
P577
2007-11-07T00:00:00Z