about
On the selection of appropriate distances for gene expression data clusteringEstimating the stochastic bifurcation structure of cellular networksEpigenetic pathways and glioblastoma treatmentDynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream AnalysisAnalysis of time-resolved gene expression measurements across individualsTranscriptome Sequencing, De Novo Assembly and Differential Gene Expression Analysis of the Early Development of Acipenser baeriClustering of High Throughput Gene Expression DataFunctional assessment of time course microarray dataPartial mixture model for tight clustering of gene expression time-courseConstrained mixture estimation for analysis and robust classification of clinical time series.DREM 2.0: Improved reconstruction of dynamic regulatory networks from time-series expression dataA method to identify differential expression profiles of time-course gene data with Fourier transformationShort time-series microarray analysis: methods and challenges.Angiogenesis interactome and time course microarray data reveal the distinct activation patterns in endothelial cells.Pattern recognition methods to relate time profiles of gene expression with phenotypic data: a comparative study.A weighted relative difference accumulation algorithm for dynamic metabolomics data: long-term elevated bile acids are risk factors for hepatocellular carcinoma.A simple approach to ranking differentially expressed gene expression time courses through Gaussian process regressionA Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data.STEM: a tool for the analysis of short time series gene expression data.Frequency-based time-series gene expression recomposition using PRIISM.Extracting binary signals from microarray time-course data.Difference-based clustering of short time-course microarray data with replicates.Distinct transcriptome responses to water limitation in isohydric and anisohydric grapevine cultivarsRidge estimation of the VAR(1) model and its time series chain graph from multivariate time-course omics data.Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data.Gene expression profiles in asbestos-exposed epithelial and mesothelial lung cell lines.Clustering time-series gene expression data using smoothing spline derivatives.Gene expression trees in lymphoid developmentStriatal proteomic analysis suggests that first L-dopa dose equates to chronic exposure.A Bayesian Change point model for differential gene expression patterns of the DosR regulon of Mycobacterium tuberculosis.Conditional clustering of temporal expression profiles.Similarity queries for temporal toxicogenomic expression profiles.A seriation approach for visualization-driven discovery of co-expression patterns in Serial Analysis of Gene Expression (SAGE) data.Gene expression during Drosophila melanogaster egg development before and after reproductive diapause.Clustered alignments of gene-expression time series data.Curve-based clustering of time course gene expression data using self-organizing maps.Extracting biologically significant patterns from short time series gene expression data.IL-3 and oncogenic Abl regulate the myeloblast transcriptome by altering mRNA stability.Model-based method for transcription factor target identification with limited data.Functional clustering of periodic transcriptional profiles through ARMA(p,q).
P2860
Q21284299-B52F710C-6281-40F1-8565-3FD24377B5A9Q21563494-2CE5A598-04C6-49A8-8CE7-F59468928AD1Q24273294-5C0D82DB-2B9A-4EA1-9560-049C624562FCQ26782685-0C1CB90C-8B5F-46C3-ACD6-1196FCD7123FQ28536632-35A5D2EA-0873-4735-B8C3-68B74232B2E3Q28607754-5C855A6A-CBEB-4DA4-A272-04715F16EB2CQ28714213-5CC989A8-6CFC-41FE-83D6-7E73EA320189Q30000987-3D5E1785-02B5-41AD-B762-92AB2B956524Q30482930-D46E3258-55A4-4E90-8323-9E07F09F2115Q30487837-937F718E-95A4-4EAB-81A7-F2DC2B2EE85DQ30557846-0CAC6F40-6BFB-4583-B02D-8E1C9B235116Q30679278-2251F7A4-D2A3-40BD-92A3-FD9707C3D4D6Q30844932-9F05E6FE-365D-4C80-9185-FA5E06FB8ED2Q30862709-204F4823-9D81-48F9-B0E0-B2084D00B7BAQ30896775-03DE8588-2DD3-4B7D-97F2-995AFE7F424CQ30908240-4D7765A9-365C-4B0A-B6A5-883E6F091EBEQ31013010-0A3643BA-BB8C-4750-8742-80365D8C1EE0Q31031718-40AEC42C-4F5A-4BFD-A62D-2D40B715FA15Q31036195-931845AF-FCB3-4BB9-B6B0-15E41A98204AQ31065378-4DCFFAC5-7D06-491E-AADC-15F34571FA02Q31113375-9AB6B3E7-136C-4BAC-9940-484ED4949E06Q31118880-0B6EC87A-CB6D-46CF-A328-2D476D239C96Q31138121-E09BDAAF-0843-44EE-8714-1AB598CE1A8BQ31146147-442859E9-DBA0-4189-A381-1043497A3EB4Q31146469-0EFFFE57-6512-48FB-B0F3-C8349E12891BQ33276240-0E539ED5-83E9-4126-BDD5-9DF4995204F5Q33294881-5A44005B-67C8-4FB5-8723-DD6C5EC940DEQ33302052-1B2FAFED-42D4-4AAF-88B4-F5D26E67393DQ33319566-E1E65504-B268-4D2F-926E-E832FEEF9C60Q33320888-5B9051B7-3461-4BD0-8846-19EA9AF92B0FQ33323312-2C94ECF1-2A6E-4AC3-AED5-013D08372FBDQ33352705-86FC9E27-8B17-48A7-B1D3-809654CEB98DQ33369091-A8BA1C11-3B45-4E44-AA88-98049FB04F21Q33451058-D9DCF51F-1448-4B83-8F69-0CF1DFAAB3E8Q33455303-F4F94CF7-8421-4CC7-9475-892A1212A10AQ33487279-C7365FF3-A791-42E5-BB2B-A73076639A68Q33495378-585D7DF1-DC82-4E4C-A3F1-A056C3C78057Q33510674-5B56F752-3A7C-42AE-8F9B-8B8EFFF02AA3Q33553059-110A3953-C32B-4EEA-B698-9FC550539A69Q33564320-49E45C10-9DC2-4B3C-AA8F-31B8B63C0575
P2860
description
2005 nî lūn-bûn
@nan
2005 թուականի Յունիսին հրատարակուած գիտական յօդուած
@hyw
2005 թվականի հունիսին հրատարակված գիտական հոդված
@hy
2005年の論文
@ja
2005年論文
@yue
2005年論文
@zh-hant
2005年論文
@zh-hk
2005年論文
@zh-mo
2005年論文
@zh-tw
2005年论文
@wuu
name
Clustering short time series gene expression data.
@ast
Clustering short time series gene expression data.
@en
type
label
Clustering short time series gene expression data.
@ast
Clustering short time series gene expression data.
@en
prefLabel
Clustering short time series gene expression data.
@ast
Clustering short time series gene expression data.
@en
P356
P1433
P1476
Clustering short time series gene expression data.
@en
P2093
Jason Ernst
P304
P356
10.1093/BIOINFORMATICS/BTI1022
P407
P478
21 Suppl 1
P577
2005-06-01T00:00:00Z