Deconvolving cell cycle expression data with complementary information.
about
In silico microdissection of microarray data from heterogeneous cell populationsRobust detection of periodic time series measured from biological systems.Microarray missing data imputation based on a set theoretic framework and biological knowledgeModel-based deconvolution of cell cycle time-series data reveals gene expression details at high resolutionH-Profile plots for the discovery and exploration of patterns in gene expression data with an application to time course data.The properties of high-dimensional data spaces: implications for exploring gene and protein expression data.Integrated assessment and prediction of transcription factor binding.Cell cycle- and chaperone-mediated regulation of H3K56ac incorporation in yeast.Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosusCombination of genomic approaches with functional genetic experiments reveals two modes of repression of yeast middle-phase meiosis genesMissing value imputation for gene expression data: computational techniques to recover missing data from available information.Identification of under-detected periodicity in time-series microarray data by using empirical mode decompositionDeconvolution of gene expression from cell populations across the C. elegans lineage.A branching process model for flow cytometry and budding index measurements in cell synchrony experiments.Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells.Branching process deconvolution algorithm reveals a detailed cell-cycle transcription programTopology and control of the cell-cycle-regulated transcriptional circuitry.Dynamic cumulative activity of transcription factors as a mechanism of quantitative gene regulation.Computational methods for estimation of cell cycle phase distributions of yeast cells.Joint principal trend analysis for longitudinal high-dimensional data.Facing the challenges of multiscale modelling of bacterial and fungal pathogen-host interactions.A stochastic model dissects cell states in biological transition processes.Temporal probabilistic modeling of bacterial compositions derived from 16S rRNA sequencing.Deconvolution of isotope signals from bundles of multiple hairs.Combined static and dynamic analysis for determining the quality of time-series expression profiles.
P2860
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P2860
Deconvolving cell cycle expression data with complementary information.
description
2004 nî lūn-bûn
@nan
2004 թուականի Օգոստոսին հրատարակուած գիտական յօդուած
@hyw
2004 թվականի օգոստոսին հրատարակված գիտական հոդված
@hy
2004年の論文
@ja
2004年論文
@yue
2004年論文
@zh-hant
2004年論文
@zh-hk
2004年論文
@zh-mo
2004年論文
@zh-tw
2004年论文
@wuu
name
Deconvolving cell cycle expression data with complementary information.
@ast
Deconvolving cell cycle expression data with complementary information.
@en
type
label
Deconvolving cell cycle expression data with complementary information.
@ast
Deconvolving cell cycle expression data with complementary information.
@en
prefLabel
Deconvolving cell cycle expression data with complementary information.
@ast
Deconvolving cell cycle expression data with complementary information.
@en
P2093
P356
P1433
P1476
Deconvolving cell cycle expression data with complementary information.
@en
P2093
David K Gifford
Roni Rosenfeld
Shlomit Farkash
P304
P356
10.1093/BIOINFORMATICS/BTH915
P407
P478
20 Suppl 1
P577
2004-08-01T00:00:00Z