CLIFF: clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts.
about
Evaluation of gene-expression clustering via mutual information distance measureSpectral biclustering of microarray data: coclustering genes and conditionsIterative class discovery and feature selection using Minimal Spanning TreesDual activation of pathways regulated by steroid receptors and peptide growth factors in primary prostate cancer revealed by Factor Analysis of microarray data.FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data.Identification of significant features in DNA microarray data.Density of points clustering, application to transcriptomic data analysis.Constructing biological pathways by a two-step counting approach.Approaches to working in high-dimensional data spaces: gene expression microarrays.A ground truth based comparative study on clustering of gene expression data.Clustering cancer gene expression data by projective clustering ensemble.Proteomic biomarker identification for diagnosis of early relapse in ovarian cancer.Prediction of the outcome of preoperative chemotherapy in breast cancer using DNA probes that provide information on both complete and incomplete responsescaBIG VISDA: modeling, visualization, and discovery for cluster analysis of genomic data.Application of gene shaving and mixture models to cluster microarray gene expression data.Statistical redundancy testing for improved gene selection in cancer classification using microarray data.Differential gene expression profile of first-generation and second-generation rapamycin-resistant allogeneic T cellsA model-based method for gene dependency measurement.Identifying responsive modules by mathematical programming: an application to budding yeast cell cycle.Improving the sensitivity of sample clustering by leveraging gene co-expression networks in variable selection.Complementary hierarchical clusteringESPD: a pattern detection model underlying gene expression profiles.City block distance and rough-fuzzy clustering for identification of co-expressed microRNAs.Assisted clustering of gene expression data using ANCut.Importance of proximity measures in clustering of cancer and miRNA datasets: proposal of an automated framework.Rough hypercuboid based supervised clustering of miRNAs.Co-authorship proximity of A. M. Turing Award and John von Neumann Medal winners to the disciplinary boundaries of computer science
P2860
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P2860
CLIFF: clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts.
description
2001 nî lūn-bûn
@nan
2001 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2001 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2001年の論文
@ja
2001年論文
@yue
2001年論文
@zh-hant
2001年論文
@zh-hk
2001年論文
@zh-mo
2001年論文
@zh-tw
2001年论文
@wuu
name
CLIFF: clustering of high-dime ...... ltering using normalized cuts.
@ast
CLIFF: clustering of high-dime ...... ltering using normalized cuts.
@en
type
label
CLIFF: clustering of high-dime ...... ltering using normalized cuts.
@ast
CLIFF: clustering of high-dime ...... ltering using normalized cuts.
@en
prefLabel
CLIFF: clustering of high-dime ...... ltering using normalized cuts.
@ast
CLIFF: clustering of high-dime ...... ltering using normalized cuts.
@en
P356
P1433
P1476
CLIFF: clustering of high-dime ...... ltering using normalized cuts.
@en
P304
P356
10.1093/BIOINFORMATICS/17.SUPPL_1.S306
P407
P478
17 Suppl 1
P577
2001-01-01T00:00:00Z