Cluster stability scores for microarray data in cancer studies.
about
A modular organization of the human intestinal mucosal microbiota and its association with inflammatory bowel diseaseModel order selection for bio-molecular data clusteringEnsemble clustering method based on the resampling similarity measure for gene expression data.Graph-based consensus clustering for class discovery from gene expression data.Discovering multi-level structures in bio-molecular data through the Bernstein inequality.Clustering cancer gene expression data by projective clustering ensemble.Reproducible clusters from microarray research: whither?Predicting survival within the lung cancer histopathological hierarchy using a multi-scale genomic model of developmentNew resampling method for evaluating stability of clusterscaBIG VISDA: modeling, visualization, and discovery for cluster analysis of genomic data.Principal component tests: applied to temporal gene expression dataMULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering.An eight-gene blood expression profile predicts the response to infliximab in rheumatoid arthritis.A highly efficient multi-core algorithm for clustering extremely large datasets.Patient-specific data fusion defines prognostic cancer subtypesCritical limitations of consensus clustering in class discoveryCluster analysis of self-monitoring blood glucose assessments in clinical islet cell transplantation for type 1 diabetesInterpolation based consensus clustering for gene expression time seriesMicrogeographic Proteomic Networks of the Human Colonic Mucosa and Their Association With Inflammatory Bowel DiseaseInformation criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments.Regulation of infection efficiency in a globally abundant marine Bacteriodetes virus.Dynamically weighted clustering with noise set.Towards sound epistemological foundations of statistical methods for high-dimensional biology.Model-based clustering with gene ranking using penalized mixtures of heavy-tailed distributions.Comprehensive gene expression analysis of rice aleurone cells: probing the existence of an alternative gibberellin receptor.clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets
P2860
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P2860
Cluster stability scores for microarray data in cancer studies.
description
2003 nî lūn-bûn
@nan
2003 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած
@hyw
2003 թվականի սեպտեմբերին 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
Cluster stability scores for microarray data in cancer studies
@nl
Cluster stability scores for microarray data in cancer studies.
@ast
Cluster stability scores for microarray data in cancer studies.
@en
type
label
Cluster stability scores for microarray data in cancer studies
@nl
Cluster stability scores for microarray data in cancer studies.
@ast
Cluster stability scores for microarray data in cancer studies.
@en
prefLabel
Cluster stability scores for microarray data in cancer studies
@nl
Cluster stability scores for microarray data in cancer studies.
@ast
Cluster stability scores for microarray data in cancer studies.
@en
P2860
P3181
P356
P1433
P1476
Cluster stability scores for microarray data in cancer studies.
@en
P2093
Debashis Ghosh
Mark Smolkin
P2860
P2888
P3181
P356
10.1186/1471-2105-4-36
P407
P577
2003-09-06T00:00:00Z
P5875
P6179
1051752573