about
Awakened by cellular stress: isolation and characterization of a novel population of pluripotent stem cells derived from human adipose tissueProportionality: a valid alternative to correlation for relative dataDefining functional DNA elements in the human genomeThe Quest for Targets Executing MYC-Dependent Cell TransformationDesign and computational analysis of single-cell RNA-sequencing experimentsBig Data Analytics in Healthcare.MicroRNA regulation of T-lymphocyte immunity: modulation of molecular networks responsible for T-cell activation, differentiation, and developmentGuidelines for the design, analysis and interpretation of 'omics' data: focus on human endometriumThe legacy of diploid progenitors in allopolyploid gene expression patternsChromatin loops as allosteric modulators of enhancer-promoter interactionsX chromosome inactivation and active X upregulation in therian mammals: facts, questions, and hypothesesFunctions of BET proteins in erythroid gene expressionc-MYC Generates Repair Errors via Increased Transcription of Alternative-NHEJ Factors, LIG3 and PARP1, in Tyrosine Kinase-Activated LeukemiasInference of quantitative models of bacterial promoters from time-series reporter gene dataTranscriptome response to elevated atmospheric CO2 concentration in the Formosan subterranean termite, Coptotermes formosanus Shiraki (Isoptera: Rhinotermitidae)Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coliIdentifying stably expressed genes from multiple RNA-Seq data setsMMDiff: quantitative testing for shape changes in ChIP-Seq data setsNormalization of RNA-sequencing data from samples with varying mRNA levelsDeciphering global signal features of high-throughput array data from cancers.Quantifying ChIP-seq data: a spiking method providing an internal reference for sample-to-sample normalization.Variation-preserving normalization unveils blind spots in gene expression profiling.Normalization of RNA-seq data using factor analysis of control genes or samplesThe concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundanceMeasuring differential gene expression with RNA-seq: challenges and strategies for data analysis.RNA-seq Data: Challenges in and Recommendations for Experimental Design and Analysis.Improving small RNA-seq by using a synthetic spike-in set for size-range quality control together with a set for data normalization.Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Dataquantro: a data-driven approach to guide the choice of an appropriate normalization methodStatistical models for RNA-seq data derived from a two-condition 48-replicate experiment.mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometryCircNet: a database of circular RNAs derived from transcriptome sequencing dataComparison of normalization and differential expression analyses using RNA-Seq data from 726 individual Drosophila melanogaster.How should we measure proportionality on relative gene expression data?Identifying network-based biomarkers of complex diseases from high-throughput data.ICN: a normalization method for gene expression data considering the over-expression of informative genes.ChIP-Seq Data Analysis to Define Transcriptional Regulatory Networks.The application of transcriptional blood signatures to enhance our understanding of the host response to infection: the example of tuberculosis.Global transcriptional and translational repression in human-embryonic-stem-cell-derived Rett syndrome neurons.Gene expression in teratogenic exposures: a new approach to understanding individual risk.
P2860
Q21133511-EB63F0EF-D9E4-48D6-A71C-5DAE20DE85ADQ21145290-B44C8B9D-692B-487D-B27E-6C4552069A17Q22066251-E42F0EC4-3A18-43AC-8260-06C4A7814B3EQ26744579-2DFFC286-E8D8-43BD-98DA-459A0C1AC570Q26751087-D8E4F4F1-FE1A-46B6-AD29-35A73EA7D9B8Q26800999-D29DC7BE-640D-4840-BC2B-1D96B2FBD767Q27003271-A033C163-03E0-4787-A68E-F1D35552B8F9Q27012789-04A334A6-4153-4C66-9912-65656FF4C495Q27026810-D987A16A-C47D-4B14-B8A1-06A0310F4533Q27320948-C162A571-CF58-4192-8AAA-DA9A0CD230BCQ28082924-BE752920-6CC6-4569-8EB8-5388E997A8C9Q28257378-8E3FF883-350F-4046-958A-E8B69812CAB0Q28259732-BD65F76D-3CFE-48E9-B721-C4BB1D9BCC9EQ28543024-C1443F51-A334-482E-B282-7F08B815598CQ28821818-F3DFF73C-18B7-489F-907E-36DE2EC14BC5Q28822192-FAE7A7E7-3D40-49DD-A433-4E8DC5449138Q30000290-744D71B7-338A-477D-89C2-588BCBA02409Q30699803-798465FD-C506-41AD-944A-EBC1C3176151Q30768621-78345B6C-AB96-4BFC-81CB-9222E17D2D5FQ30792298-108FCA13-3FA9-4453-BA68-83FF8D3ED1D1Q30795466-4F6258FF-F31A-4F36-A420-01A604723484Q30841312-4C2E630F-7D36-461F-B150-1973B2E51C44Q30844396-45A47667-D843-472C-949E-B75989492501Q30844402-3F32816B-5553-4A1C-8311-CD298FEAA3AFQ30853974-D2B818AA-3977-4574-91BD-752EB06530FFQ30856802-61575287-F13B-4CB6-B837-5FE170E1A5A7Q30931084-70B9A4CA-9338-4A36-9E81-F65D018DA9EDQ30943430-6A6D1E45-B3C2-4E47-8A1F-3929ECE7E16CQ30964731-C782C262-8F7B-4C28-A3EC-EDE93B3F3467Q30982380-46A4977E-AEB1-423C-9FAA-5F855E33543AQ30994135-40DDD45B-83BF-4193-9865-C97EEFBD5565Q31003645-DA342E6E-C4D2-41C8-95C5-208676D68DADQ31035102-067500B1-B414-45B7-BBDB-58BEEE5E8D2FQ31036697-DCD753B7-F6BB-420C-ABC9-6C20F8B264DFQ31038202-FA483AA6-C134-4DF6-9503-AE65E94EBE6EQ31117147-B778E45F-6D87-4862-BF91-745177CFDBB7Q31153870-19BD4185-4495-46A5-9086-FCADEA266DEDQ33624570-CFF1D0A6-6032-44A3-9A7E-C28EEA794579Q33740090-FC3CCBB7-8137-4C8A-9E04-7D9D411DF975Q33791715-EE44EFE6-BC84-416E-B4FC-5824FCDA63F7
P2860
description
2012 nî lūn-bûn
@nan
2012 թուականի Հոկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2012 թվականի հոտեմբերին հրատարակված գիտական հոդված
@hy
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
@wuu
name
Revisiting global gene expression analysis
@ast
Revisiting global gene expression analysis
@en
Revisiting global gene expression analysis
@nl
type
label
Revisiting global gene expression analysis
@ast
Revisiting global gene expression analysis
@en
Revisiting global gene expression analysis
@nl
prefLabel
Revisiting global gene expression analysis
@ast
Revisiting global gene expression analysis
@en
Revisiting global gene expression analysis
@nl
P2093
P2860
P1433
P1476
Revisiting global gene expression analysis
@en
P2093
Alla A Sigova
Charles Y Lin
Christopher B Burge
David A Orlando
David L Levens
Jakob Lovén
Peter B Rahl
Tong Ihn Lee
P2860
P304
P356
10.1016/J.CELL.2012.10.012
P407
P50
P577
2012-10-01T00:00:00Z