Studying and modelling dynamic biological processes using time-series gene expression data.
about
Effects of insufficient sleep on circadian rhythmicity and expression amplitude of the human blood transcriptomeGenome-Wide Transcriptional Response of Saccharomyces cerevisiae to Stress-Induced PerturbationsDynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream AnalysisNext-generation analysis of gene expression regulation--comparing the roles of synthesis and degradationThe analytical landscape of static and temporal dynamics in transcriptome dataAdvanced Applications of RNA Sequencing and ChallengesLearning from Co-expression Networks: Possibilities and ChallengesFrom the exposome to mechanistic understanding of chemical-induced adverse effectsComputationally Modeling Lipid Metabolism and Aging: A Mini-reviewStudying Dynamic Features in Myocardial Infarction Progression by Integrating miRNA-Transcription Factor Co-Regulatory Networks and Time-Series RNA Expression Data from Peripheral Blood Mononuclear CellsAnalysis of time-resolved gene expression measurements across individualsNetwork analysis of breast cancer progression and reversal using a tree-evolving network algorithmInference of quantitative models of bacterial promoters from time-series reporter gene dataInferring gene dependency network specific to phenotypic alteration based on gene expression data and clinical information of breast cancerApplications for single cell trajectory analysis in inner ear development and regeneration.Visualizing time-related data in biology, a reviewRobust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks.SwitchFinder - a novel method and query facility for discovering dynamic gene expression patterns.Diverse activities of viral cis-acting RNA regulatory elements revealed using multicolor, long-term, single-cell imagingSMARTS: reconstructing disease response networks from multiple individuals using time series gene expression dataPattern recognition methods to relate time profiles of gene expression with phenotypic data: a comparative study.Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Dataquantro: a data-driven approach to guide the choice of an appropriate normalization methodFunPat: function-based pattern analysis on RNA-seq time series data.Inferring interaction type in gene regulatory networks using co-expression data.Decoding Cellular Dynamics in Epidermal Growth Factor Signaling Using a New Pathway-Based Integration Approach for Proteomics and Transcriptomics Data.Identifying network-based biomarkers of complex diseases from high-throughput data.Statistical modeling of isoform splicing dynamics from RNA-seq time series data.Natural Cubic Spline Regression Modeling Followed by Dynamic Network Reconstruction for the Identification of Radiation-Sensitivity Gene Association Networks from Time-Course Transcriptome Data.A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma.Application of dynamic topic models to toxicogenomics dataImpulseDE: detection of differentially expressed genes in time series data using impulse models.DTNI: a novel toxicogenomics data analysis tool for identifying the molecular mechanisms underlying the adverse effects of toxic compounds.TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes.Prioritizing biological pathways by recognizing context in time-series gene expression dataElucidation of the sequential transcriptional activity in Escherichia coli using time-series RNA-seq dataDynamic microbe and molecule networks in a mouse model of colitis-associated colorectal cancer.DTWscore: differential expression and cell clustering analysis for time-series single-cell RNA-seq data.A computational algorithm for functional clustering of proteome dynamics during developmentTranscriptional atlas of cardiogenesis maps congenital heart disease interactome.
P2860
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P2860
Studying and modelling dynamic biological processes using time-series gene expression data.
description
2012 nî lūn-bûn
@nan
2012 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2012 թվականի հուլիսին 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
Studying and modelling dynamic ...... e-series gene expression data.
@ast
Studying and modelling dynamic ...... e-series gene expression data.
@en
Studying and modelling dynamic ...... e-series gene expression data.
@nl
type
label
Studying and modelling dynamic ...... e-series gene expression data.
@ast
Studying and modelling dynamic ...... e-series gene expression data.
@en
Studying and modelling dynamic ...... e-series gene expression data.
@nl
prefLabel
Studying and modelling dynamic ...... e-series gene expression data.
@ast
Studying and modelling dynamic ...... e-series gene expression data.
@en
Studying and modelling dynamic ...... e-series gene expression data.
@nl
P50
P356
P1476
Studying and modelling dynamic ...... e-series gene expression data.
@en
P2888
P304
P356
10.1038/NRG3244
P577
2012-07-18T00:00:00Z
P6179
1009073159