Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes.
about
Clustering cancer gene expression data: a comparative studyA Normalized Tree Index for identification of correlated clinical parameters in microarray experimentsRNA-Seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clusteringA new unsupervised gene clustering algorithm based on the integration of biological knowledge into expression data.Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm.Bayesian hierarchical clustering for studying cancer gene expression data with unknown statisticsImproving clustering with metabolic pathway data.Recursive cluster elimination (RCE) for classification and feature selection from gene expression data.CLEAN: CLustering Enrichment ANalysis.Segmentation of biological multivariate time-series data.DGEclust: differential expression analysis of clustered count data.Knowledge-Based Analysis for Detecting Key Signaling Events from Time-Series Phosphoproteomics Data.Bayesian hierarchical clustering for microarray time series data with replicates and outlier measurements.FatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experimentsSimcluster: clustering enumeration gene expression data on the simplex space.Graph-based consensus clustering for class discovery from gene expression data.A ground truth based comparative study on clustering of gene expression data.GEPAS, a web-based tool for microarray data analysis and interpretation.Evaluation of clustering algorithms for gene expression data.Unravelling the hidden heterogeneities of diffuse large B-cell lymphoma based on coupled two-way clusteringInferring biological functions and associated transcriptional regulators using gene set expression coherence analysisNew resampling method for evaluating stability of clusterscaBIG VISDA: modeling, visualization, and discovery for cluster analysis of genomic data.DISCLOSE : DISsection of CLusters Obtained by SEries of transcriptome data using functional annotations and putative transcription factor binding sites.R/BHC: fast Bayesian hierarchical clustering for microarray dataModeling co-expression across species for complex traits: insights to the difference of human and mouse embryonic stem cellsDiscovering transcriptional modules by Bayesian data integration.Classification of protein kinases on the basis of both kinase and non-kinase regionsStatistical inference and reverse engineering of gene regulatory networks from observational expression data.Inferring robust gene networks from expression data by a sensitivity-based incremental evolution method.A systematic comparison of genome-scale clustering algorithms.Whole-exome sequencing of primary plasma cell leukemia discloses heterogeneous mutational patterns.How and when should interactome-derived clusters be used to predict functional modules and protein function?Mining the modular structure of protein interaction networksIncorporation of biological knowledge into distance for clustering genesFunctional profiling and gene expression analysis of chromosomal copy number alterationsMeta-analysis of cell- specific transcriptomic data using fuzzy c-means clustering discovers versatile viral responsive genes.Selecting anti-epileptic drugs: a pediatric epileptologist's view, a computer's view.Class-specific correlations of gene expressions: identification and their effects on clustering analyses.Fast approximate hierarchical clustering using similarity heuristics.
P2860
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P2860
Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes.
description
2006 nî lūn-bûn
@nan
2006 թուականի Օգոստոսին հրատարակուած գիտական յօդուած
@hyw
2006 թվականի օգոստոսին հրատարակված գիտական հոդված
@hy
2006年の論文
@ja
2006年論文
@yue
2006年論文
@zh-hant
2006年論文
@zh-hk
2006年論文
@zh-mo
2006年論文
@zh-tw
2006年论文
@wuu
name
Methods for evaluating cluster ...... nce set of functional classes.
@ast
Methods for evaluating cluster ...... nce set of functional classes.
@en
type
label
Methods for evaluating cluster ...... nce set of functional classes.
@ast
Methods for evaluating cluster ...... nce set of functional classes.
@en
prefLabel
Methods for evaluating cluster ...... nce set of functional classes.
@ast
Methods for evaluating cluster ...... nce set of functional classes.
@en
P2860
P356
P1433
P1476
Methods for evaluating cluster ...... nce set of functional classes.
@en
P2093
Somnath Datta
Susmita Datta
P2860
P2888
P356
10.1186/1471-2105-7-397
P577
2006-08-31T00:00:00Z
P5875
P6179
1033901287