Using hidden Markov models to analyze gene expression time course data.
about
Integrated analysis of gene expression by Association Rules DiscoveryDiscovery of Time-Delayed Gene Regulatory Networks based on temporal gene expression profiling.The analytical landscape of static and temporal dynamics in transcriptome dataModeling and Classification of Kinetic Patterns of Dynamic Metabolic Biomarkers in Physical ActivitypGQL: A probabilistic graphical query language for gene expression time coursesFunctional assessment of time course microarray dataEstimating equation-based causality analysis with application to microarray time series data.Constrained mixture estimation for analysis and robust classification of clinical time series.A method to identify differential expression profiles of time-course gene data with Fourier transformationSwitchFinder - a novel method and query facility for discovering dynamic gene expression patterns.CLUSTERnGO: a user-defined modelling platform for two-stage clustering of time-series data.DNA microarray data imputation and significance analysis of differential expression.STEM: a tool for the analysis of short time series gene expression data.Cluster-based network model for time-course gene expression data.Analysis of time-series gene expression data: methods, challenges, and opportunities.Extracting binary signals from microarray time-course data.Post hoc pattern matching: assigning significance to statistically defined expression patterns in single channel microarray data.Clustering of time-course gene expression data using functional data analysis.The wavelet-based cluster analysis for temporal gene expression dataApplication of dynamic topic models to toxicogenomics dataSemi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data.Identification of gene expression patterns using planned linear contrastsModularity and dynamics of cellular networksMining and state-space modeling and verification of sub-networks from large-scale biomolecular networks.Conditional clustering of temporal expression profiles.A combinatorial approach to determine the context-dependent role in transcriptional and posttranscriptional regulation in Arabidopsis thalianaIdentification of global transcriptional dynamicsExtracting biologically significant patterns from short time series gene expression data.Dealing with missing values in large-scale studies: microarray data imputation and beyond.A biclustering algorithm based on a bicluster enumeration tree: application to DNA microarray data.Comparative analysis of missing value imputation methods to improve clustering and interpretation of microarray experiments.Dynamic metabolomic data analysis: a tutorial reviewMulticonstrained gene clustering based on generalized projections.Time-series clustering of gene expression in irradiated and bystander fibroblasts: an application of FBPA clustering.A platform for processing expression of short time series (PESTS).Estimating developmental states of tumors and normal tissues using a linear time-ordered model.Bayesian model-based tight clustering for time course data.Detection and interpretation of metabolite-transcript coresponses using combined profiling data.Reverse engineering dynamic temporal models of biological processes and their relationships.Pattern-driven neighborhood search for biclustering of microarray data
P2860
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P2860
Using hidden Markov models to analyze gene expression time course data.
description
2003 nî lūn-bûn
@nan
2003 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2003 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2003年の論文
@ja
2003年論文
@yue
2003年論文
@zh-hant
2003年論文
@zh-hk
2003年論文
@zh-mo
2003年論文
@zh-tw
2003年论文
@wuu
name
Using hidden Markov models to analyze gene expression time course data.
@ast
Using hidden Markov models to analyze gene expression time course data.
@en
type
label
Using hidden Markov models to analyze gene expression time course data.
@ast
Using hidden Markov models to analyze gene expression time course data.
@en
prefLabel
Using hidden Markov models to analyze gene expression time course data.
@ast
Using hidden Markov models to analyze gene expression time course data.
@en
P356
P1433
P1476
Using hidden Markov models to analyze gene expression time course data.
@en
P2093
Alexander Schönhuth
Christine Steinhoff
P304
P356
10.1093/BIOINFORMATICS/BTG1036
P407
P478
19 Suppl 1
P577
2003-01-01T00:00:00Z