about
Integrated network analysis of transcriptomic and proteomic data in psoriasisLarge-scale integration of microarray data reveals genes and pathways common to multiple cancer typesReconsideration of in-silico siRNA design based on feature selection: a cross-platform data integration perspectiveEcosystem size matters: the dimensionality of intralacustrine diversification in Icelandic stickleback is predicted by lake sizeDevelopment of synchronous VHL syndrome tumors reveals contingencies and constraints to tumor evolutionIntegrating human omics data to prioritize candidate genesIntegrated analysis of transcriptomic and proteomic dataInter-platform concordance of gene expression data for the prediction of chemical mode of actionComparison and evaluation of pathway-level aggregation methods of gene expression data.Network and data integration for biomarker signature discovery via network smoothed T-statistics.Performance evaluation of image segmentation algorithms on microscopic image data.Ensemble Methods for MiRNA Target Prediction from Expression Data.Validation of reference genes for quantitative real-time PCR during leaf and flower development in Petunia hybridaAn adaptive optimal ensemble classifier via bagging and rank aggregation with applications to high dimensional data.Asymmetric microarray data produces gene lists highly predictive of research literature on multiple cancer types.Selection of suitable endogenous reference genes for qPCR in kidney and hypothalamus of rats under testosterone influence.Detection of COPB2 as a KRAS synthetic lethal partner through integration of functional genomics screens.Reference gene validation for qPCR in rat carotid body during postnatal development.Identification of superior reference genes for data normalisation of expression studies via quantitative PCR in hybrid roses (Rosa hybrida)Cancer gene prioritization for targeted resequencing using FitSNP scoresReference genes for real-time PCR quantification of microRNAs and messenger RNAs in rat models of hepatotoxicity.Selection and validation of reference genes for studying stress-related agarwood formation of Aquilaria sinensis.Expressed repeat elements improve RT-qPCR normalization across a wide range of zebrafish gene expression studiesCoex-Rank: An approach incorporating co-expression information for combined analysis of microarray data.Reference gene validation for quantitative RT-PCR during biotic and abiotic stresses in Vitis viniferaSystematic analysis of the gene expression in the livers of nonalcoholic steatohepatitis: implications on potential biomarkers and molecular pathological mechanism.Real-time qPCR identifies suitable reference genes for Borna disease virus-infected rat cortical neurons.Identification and validation of reference genes for transcript normalization in strawberry (Fragaria × ananassa) defense responses.Effective Alu repeat based RT-Qpcr normalization in cancer cell perturbation experimentsIdentifying potential cancer driver genes by genomic data integrationReference genes selection for quantitative real-time PCR using RankAggreg method in different tissues of Capra hircus.Finding genetic overlaps among diseases based on ranked gene lists.A Molecular-Level Landscape of Diet-Gut Microbiome Interactions: Toward Dietary Interventions Targeting Bacterial GenesCentiServer: A Comprehensive Resource, Web-Based Application and R Package for Centrality AnalysisComprehensive literature review and statistical considerations for microarray meta-analysisScreening Ingredients from Herbs against Pregnane X Receptor in the Study of Inductive Herb-Drug Interactions: Combining Pharmacophore and Docking-Based Rank Aggregation.Identification of Reference Genes for Quantitative Gene Expression Studies in a Non-Model Tree Pistachio (Pistacia vera L.).Evaluation of Appropriate Reference Genes for Reverse Transcription-Quantitative PCR Studies in Different Tissues of a Desert Poplar via Comparision of Different AlgorithmsDMirNet: Inferring direct microRNA-mRNA association networks.Monitoring seasonal influenza epidemics by using internet search data with an ensemble penalized regression model.
P2860
Q24610642-8991C7E8-CC2B-46FA-BF64-D5ADD83DE037Q28301260-A8B68A3E-EC0C-48D0-A3D2-F7B697396239Q28483920-BDE40345-21D8-4E2E-9F49-DCA0D5EFAA5BQ28597842-D68BE0A5-F6D5-48D2-B06F-F8ABB24E863BQ28655705-E29A06E2-EDD7-41DA-9DAB-A52E68B1D03DQ28660898-ADAB3EB2-2AC4-4B8F-8705-868CA3C0F45DQ28707991-BDAC43B3-53FC-4913-8B77-FFE35BF033A3Q28817067-E110116B-67E8-4002-88B4-70FD2BA09F91Q30584130-ABBBA8A4-2647-47A3-B16F-48278678967FQ30665910-05CD2EA9-1D57-4661-8FE7-A25B0B7E7C3DQ30853603-B36DCC25-326D-45BF-BB42-33BC4D53760AQ30976943-83F59479-F350-4A5E-B874-F098A5DD4F24Q33522477-D222C972-0D54-45AF-B4C4-7B4619A3D040Q33661199-2C9EF286-92D3-458F-965E-C3897196C531Q33704423-8FD46A06-919E-4519-A67D-217D9648628DQ33772868-06C1DF17-2F89-4DA3-BDF1-569E31E0FE8AQ33798340-E0995644-BE32-4248-8ECF-2A63BA0D9CEBQ34056691-435C199B-43C2-4F5D-9AB9-216A01D228C7Q34084973-506E5B86-B436-432A-849A-76F17D7DAB5DQ34187077-E2777F9C-EDD7-4E11-898E-69A3DB4C1CE5Q34260117-E99B6437-12C0-4189-B083-2F1F4EBC7E14Q34296839-1E93CF7B-AFA2-435A-9327-8067CCAB3455Q34332150-17F5818B-445A-4874-AF5B-8315D414B671Q34355516-B6D64EBF-E63A-4D90-9A9A-14754B51B5BCQ34395853-913FA170-CDAB-4652-A85A-4CDF8BA602F9Q34540092-089024F2-CC48-44D8-B502-107D8AF58DAFQ34833727-8162709C-D403-4108-93DB-827E1FDEFF5EQ34935432-FA6DD1D9-51FA-4C88-AC87-D3515C523C47Q34972240-18BD3089-9302-4039-974F-5540129F4D29Q35068968-1685CF3B-E969-4A6B-979B-0580E7BCE375Q35072976-3121DD8C-5695-44F5-964F-9D81FA303FF0Q35078429-DD1E952B-07E6-4E74-9F2F-7FEFE0904E02Q35823443-E98771DF-BCBB-4288-BF37-C12630EC5F9DQ35842128-7796C971-50E2-4000-8174-3C5D590FEBDFQ35955982-402B818B-113E-4024-85BA-50C5D1365200Q35960314-E1665E5C-15A3-4FCF-B185-A5BFE0F0688BQ36053879-96B2EC0A-55CB-4EC0-82BC-EBF338F1E0CEQ36183982-6BAB29E3-B9B0-473C-8F45-7202AC267309Q36255079-257AF1BA-D793-4DFE-8B5E-18C731D6027AQ36350635-E4D7AF4E-73BB-41AF-BF7C-91B05E045411
P2860
description
2009 nî lūn-bûn
@nan
2009 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2009 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
2009年の論文
@ja
2009年論文
@yue
2009年論文
@zh-hant
2009年論文
@zh-hk
2009年論文
@zh-mo
2009年論文
@zh-tw
2009年论文
@wuu
name
RankAggreg, an R package for weighted rank aggregation.
@ast
RankAggreg, an R package for weighted rank aggregation.
@en
type
label
RankAggreg, an R package for weighted rank aggregation.
@ast
RankAggreg, an R package for weighted rank aggregation.
@en
prefLabel
RankAggreg, an R package for weighted rank aggregation.
@ast
RankAggreg, an R package for weighted rank aggregation.
@en
P2093
P2860
P356
P1433
P1476
RankAggreg, an R package for weighted rank aggregation.
@en
P2093
Somnath Datta
Susmita Datta
Vasyl Pihur
P2860
P2888
P356
10.1186/1471-2105-10-62
P577
2009-02-19T00:00:00Z
P5875
P6179
1052717070