Time series expression analyses using RNA-seq: a statistical approach.
about
Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream AnalysisThe analytical landscape of static and temporal dynamics in transcriptome dataEfficient extraction of small and large RNAs in bacteria for excellent total RNA sequencing and comprehensive transcriptome analysisStratification of gene coexpression patterns and GO function mining for a RNA-Seq data series.Statistical inference for time course RNA-Seq data using a negative binomial mixed-effect model.Independent Component Analysis (ICA) based-clustering of temporal RNA-seq data.Differential Gene Expression (DEX) and Alternative Splicing Events (ASE) for Temporal Dynamic Processes Using HMMs and Hierarchical Bayesian Modeling Approaches.Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response.A review of subsequence time series clustering.
P2860
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P2860
Time series expression analyses using RNA-seq: a statistical approach.
description
2013 nî lūn-bûn
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2013年の論文
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2013年学术文章
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name
Time series expression analyses using RNA-seq: a statistical approach.
@en
Time series expression analyses using RNA-seq: a statistical approach.
@nl
type
label
Time series expression analyses using RNA-seq: a statistical approach.
@en
Time series expression analyses using RNA-seq: a statistical approach.
@nl
prefLabel
Time series expression analyses using RNA-seq: a statistical approach.
@en
Time series expression analyses using RNA-seq: a statistical approach.
@nl
P2093
P2860
P356
P1476
Time series expression analyses using RNA-seq: a statistical approach.
@en
P2093
Gregory Grabowski
Hongyu Zhao
James P Noonan
Seongho Song
Sunghee Oh
P2860
P304
P356
10.1155/2013/203681
P407
P577
2013-03-24T00:00:00Z