Judging the quality of gene expression-based clustering methods using gene annotation.
about
Paradigm of tunable clustering using Binarization of Consensus Partition Matrices (Bi-CoPaM) for gene discoveryIdentification of a gene regulatory network necessary for the initiation of oligodendrocyte differentiationOn the selection of appropriate distances for gene expression data clusteringGlobal gene expression of Prochlorococcus ecotypes in response to changes in nitrogen availabilityConsensus clustering and functional interpretation of gene-expression dataSelection of informative clusters from hierarchical cluster tree with gene classesInfluence of microarrays experiments missing values on the stability of gene groups by hierarchical clusteringFinding groups in gene expression dataAn approach for clustering gene expression data with error information.multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome ProfilesNetwork motif-based identification of transcription factor-target gene relationships by integrating multi-source biological dataIdentification of genes required for cellulose synthesis by regression analysis of public microarray data sets.Toward reliable biomarker signatures in the age of liquid biopsies - how to standardize the small RNA-Seq workflowRecursive expectation-maximization clustering: a method for identifying buffering mechanisms composed of phenomic modules.Quality assessment and data handling methods for Affymetrix Gene 1.0 ST arrays with variable RNA integrity.Recursive cluster elimination (RCE) for classification and feature selection from gene expression data.LSimpute: accurate estimation of missing values in microarray data with least squares methodsComputational strategies for analyzing data in gene expression microarray experiments.Improving missing value estimation in microarray data with gene ontology.A classification based framework for quantitative description of large-scale microarray data.Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes.Identifying functional gene sets from hierarchically clustered expression data: map of abiotic stress regulated genes in Arabidopsis thaliana.Analysis of time-series gene expression data: methods, challenges, and opportunities.Difference-based clustering of short time-course microarray data with replicates.Integration of known transcription factor binding site information and gene expression data to advance from co-expression to co-regulation.DRAGON and DRAGON view: information annotation and visualization tools for large-scale expression data.A ground truth based comparative study on clustering of gene expression data.Validation and functional annotation of expression-based clusters based on gene ontology.A transversal approach to predict gene product networks from ontology-based similarityUnravelling the hidden heterogeneities of diffuse large B-cell lymphoma based on coupled two-way clusteringTranscription factor target prediction using multiple short expression time series from Arabidopsis thaliana.Analysis of a Gibbs sampler method for model-based clustering of gene expression data.Employing conservation of co-expression to improve functional inferenceExpression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification.DISCLOSE : DISsection of CLusters Obtained by SEries of transcriptome data using functional annotations and putative transcription factor binding sites.Application of gene shaving and mixture models to cluster microarray gene expression data.Generation of Gene Ontology benchmark datasets with various types of positive signal.Gene module identification from microarray data using nonnegative independent component analysisPersistent donor cell gene expression among human induced pluripotent stem cells contributes to differences with human embryonic stem cells.SWISS MADE: Standardized WithIn Class Sum of Squares to evaluate methodologies and dataset elements.
P2860
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P2860
Judging the quality of gene expression-based clustering methods using gene annotation.
description
2002 nî lūn-bûn
@nan
2002 թուականի Հոկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2002 թվականի հոտեմբերին հրատարակված գիտական հոդված
@hy
2002年の論文
@ja
2002年論文
@yue
2002年論文
@zh-hant
2002年論文
@zh-hk
2002年論文
@zh-mo
2002年論文
@zh-tw
2002年论文
@wuu
name
Judging the quality of gene expression-based clustering methods using gene annotation.
@ast
Judging the quality of gene expression-based clustering methods using gene annotation.
@en
Judging the quality of gene expression-based clustering methods using gene annotation.
@nl
type
label
Judging the quality of gene expression-based clustering methods using gene annotation.
@ast
Judging the quality of gene expression-based clustering methods using gene annotation.
@en
Judging the quality of gene expression-based clustering methods using gene annotation.
@nl
prefLabel
Judging the quality of gene expression-based clustering methods using gene annotation.
@ast
Judging the quality of gene expression-based clustering methods using gene annotation.
@en
Judging the quality of gene expression-based clustering methods using gene annotation.
@nl
P2860
P356
P1433
P1476
Judging the quality of gene expression-based clustering methods using gene annotation.
@en
P2093
Francis D Gibbons
P2860
P304
P356
10.1101/GR.397002
P577
2002-10-01T00:00:00Z