A simple approach to ranking differentially expressed gene expression time courses through Gaussian process regression
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Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream AnalysisReverse engineering a mouse embryonic stem cell-specific transcriptional network reveals a new modulator of neuronal differentiationA method to identify differential expression profiles of time-course gene data with Fourier transformationInference of RNA polymerase II transcription dynamics from chromatin immunoprecipitation time course dataMyosin7a deficiency results in reduced retinal activity which is improved by gene therapy.TTCA: an R package for the identification of differentially expressed genes in time course microarray dataBayesian prediction of RNA translation from ribosome profilingIdentification of potential new treatment response markers and therapeutic targets using a Gaussian process-based method in lapatinib insensitive breast cancer models.Protein biogenesis machinery is a driver of replicative aging in yeastHierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters.Differential reconstructed gene interaction networks for deriving toxicity threshold in chemical risk assessmentGaussian process test for high-throughput sequencing time series: application to experimental evolution.Inferring the perturbation time from biological time course data.Computational approaches for interpreting scRNA-seq data.Tradeoffs between Dense and Replicate Sampling Strategies for High-Throughput Time Series Experiments.DynOmics to identify delays and co-expression patterns across time course experiments.Helper-dependent adenoviral vectors for liver-directed gene therapy of primary hyperoxaluria type 1.Analysis of differential splicing suggests different modes of short-term splicing regulation.Time series expression analyses using RNA-seq: a statistical approach.A stochastic model dissects cell states in biological transition processes.Detecting time periods of differential gene expression using Gaussian processes: an application to endothelial cells exposed to radiotherapy dose fraction.High-throughput screening identifies kinase inhibitors that increase dual AAV vectors transduction <i>in vitro</i> and in mouse retina.GPrank: an R package for detecting dynamic elements from genome-wide time seriesWTFgenes: What's The Function of these genes? Static sites for model-based gene set analysisBranch-recombinant Gaussian processes for analysis of perturbations in biological time series
P2860
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P2860
A simple approach to ranking differentially expressed gene expression time courses through Gaussian process regression
description
2011 nî lūn-bûn
@nan
2011 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի մայիսին հրատարակված գիտական հոդված
@hy
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
name
A simple approach to ranking d ...... gh Gaussian process regression
@ast
A simple approach to ranking d ...... gh Gaussian process regression
@en
type
label
A simple approach to ranking d ...... gh Gaussian process regression
@ast
A simple approach to ranking d ...... gh Gaussian process regression
@en
prefLabel
A simple approach to ranking d ...... gh Gaussian process regression
@ast
A simple approach to ranking d ...... gh Gaussian process regression
@en
P2860
P356
P1433
P1476
A simple approach to ranking d ...... gh Gaussian process regression
@en
P2093
Alfredo A Kalaitzis
P2860
P2888
P356
10.1186/1471-2105-12-180
P577
2011-05-20T00:00:00Z
P5875
P6179
1018244414