Improved biclustering of microarray data demonstrated through systematic performance tests

DOI10.1016/j.csda.2004.02.003zbMath1429.62267OpenAlexW2023416945WikidataQ57933586 ScholiaQ57933586MaRDI QIDQ125777

Trevor Bailey, Wojtek Krzanowski, Heather Turner, Wojtek Janusz Krzanowski, Trevor C. Bailey, Heather L. Turner

Publication date: February 2005

Published in: Computational Statistics & Data Analysis, Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2004.02.003

zbMATH Keywords

performance evaluationoverlapping clusteringbiclusteringtwo-way clusteringartificial microarray databicluster quality measures

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Genetics and epigenetics (92D10)

Related Items (19)

Biclustering of ARMA time series ⋮ Nonparametric Bayesian Two-Level Clustering for Subject-Level Single-Cell Expression Data ⋮ Additive biclustering: a comparison of one new and two existing ALS algorithms ⋮ Multi-objective evolutionary biclustering of gene expression data ⋮ Stability-based validation of bicluster solutions ⋮ Convex biclustering ⋮ Topological biclustering ARTMAP for identifying within bicluster relationships ⋮ Regularized matrix data clustering and its application to image analysis ⋮ Biclustering via semiparametric Bayesian inference ⋮ The Gibbs-plaid biclustering model ⋮ The penalized biclustering model and related algorithms ⋮ A unifying model for biclustering ⋮ Biclustering with heterogeneous variance ⋮ Finding large average submatrices in high dimensional data ⋮ Complexity of modification problems for reciprocal best match graphs ⋮ Local spatial biclustering and prediction of urban juvenile delinquency and recidivism ⋮ On two‐way Bayesian agglomerative clustering of gene expression data ⋮ biclust ⋮ Unnamed Item

Uses Software

• R
• limma

Cites Work

• Unnamed Item
• Unnamed Item
• GENNCLUS: New models for general nonhierarchical clustering analysis
• MAPCLUS: A mathematical programming approach to fitting the ADCLUS model
• An alternating combinatorial optimization approach to fitting the INDCLUS and generalized INDCLUS models
• Statistical inference for simultaneous clustering of gene expression data
• A simple method for generating additive clustering models with limited complexity
• Additive clustering and qualitative factor analysis methods for similarity matrices
• An extraction and regularization approach to additive clustering

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