Genome Biology

official impact factor 6.89

Open Access

A prediction-based resampling method for estimating the number of clusters in a dataset

Sandrine Dudoit* and Jane Fridlyand

Genome Biology 2002, 3:research0036-research0036.21 doi:10.1186/gb-2002-3-7-research0036

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Research article   Open Access

Transcriptional profiling of fetal hypothalamic TRH neurons

Magdalena Guerra-Crespo, Carlos Pérez-Monter, Sarath Janga, Santiago Castillo-Ramírez, Rosa Gutiérrez-Rios, Patricia Joseph-Bravo, Leonor Pérez-Martínez, Jean-Louis Charli BMC Genomics 2011, 12:222 (10 May 2011)

Methodology article   Open Access

MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering

Eun-Youn Kim, Seon-Young Kim, Daniel Ashlock, Dougu Nam BMC Bioinformatics 2009, 10:260 (22 August 2009)

Research article   Open Access

Natural genetic variation in transcriptome reflects network structure inferred with major effect mutations: insulin/TOR and associated phenotypes in Drosophila melanogaster

Sergey V Nuzhdin, Jennifer A Brisson, Andrew Pickering, Marta L Wayne, Lawrence G Harshman, Lauren M McIntyre BMC Genomics 2009, 10:124 (24 March 2009)

Naturally-occurring variation in Drosophila insulin/TOR signaling pathway genes affects expression and phenotypes similarly to gene knockouts, but the sparse picture of regulatory networks derived from knockout studies on inbred lines is probably oversimplified.

Methodology article   Open Access Highly Accessed

NIFTI: An evolutionary approach for finding number of clusters in microarray data

Sudhakar Jonnalagadda, Rajagopalan Srinivasan BMC Bioinformatics 2009, 10:40 (30 January 2009)

Software   Open Access Highly Accessed

WGCNA: an R package for weighted correlation network analysis

Peter Langfelder, Steve Horvath BMC Bioinformatics 2008, 9:559 (29 December 2008)

Research article   Open Access Highly Accessed

Computational cluster validation for microarray data analysis: experimental assessment of Clest, Consensus Clustering, Figure of Merit, Gap Statistics and Model Explorer

Raffaele Giancarlo, Davide Scaturro, Filippo Utro BMC Bioinformatics 2008, 9:462 (29 October 2008)

Five popular measures for validation of clusters in microarray data vary in their predictive power and computational speed but some approximations to these algorithms can perform almost as well but very much more quickly.

Research   Open Access

Genetic weighted k-means algorithm for clustering large-scale gene expression data

Fang-Xiang Wu BMC Bioinformatics 2008, 9(Suppl 6):S12 (28 May 2008)

Research   Open Access

Discovering multi–level structures in bio-molecular data through the Bernstein inequality

Alberto Bertoni, Giorgio Valentini BMC Bioinformatics 2008, 9(Suppl 2):S4 (26 March 2008)

Research   Open Access

Model order selection for bio-molecular data clustering

Alberto Bertoni, Giorgio Valentini BMC Bioinformatics 2007, 8(Suppl 2):S7 (3 May 2007)

Methodology article   Open Access

Cluster analysis of protein array results via similarity of Gene Ontology annotation

Cheryl Wolting, C Jane McGlade, David Tritchler BMC Bioinformatics 2006, 7:338 (12 July 2006)

Research article   Open Access Highly Accessed

Classification and risk stratification of invasive breast carcinomas using a real-time quantitative RT-PCR assay

Laurent Perreard, Cheng Fan, John F Quackenbush, Michael Mullins, Nicholas P Gauthier, Edward Nelson, Mary Mone, Heidi Hansen, Saundra S Buys, Karen Rasmussen, Alejandra Orrico, Donna Dreher, Rhonda Walters, Joel Parker, Zhiyuan Hu, Xiaping He, Juan P Palazzo, Olufunmilayo I Olopade, Aniko Szabo, Charles M Perou, Philip S Bernard Breast Cancer Research 2006, 8:R23 (20 April 2006)

Research article   Open Access Highly Accessed

Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts

Yudi Pawitan, Judith Bjöhle, Lukas Amler, Anna-Lena Borg, Suzanne Egyhazi, Per Hall, Xia Han, Lars Holmberg, Fei Huang, Sigrid Klaar, Edison T Liu, Lance Miller, Hans Nordgren, Alexander Ploner, Kerstin Sandelin, Peter M Shaw, Johanna Smeds, Lambert Skoog, Sara Wedrén, Jonas Bergh Breast Cancer Research 2005, 7:R953-R964 (3 October 2005)

Methodology article   Open Access

Statistical significance for hierarchical clustering in genetic association and microarray expression studies

Mark A Levenstien, Yaning Yang, Jürg Ott BMC Bioinformatics 2003, 4:62 (11 December 2003)

Methodology article   Open Access

Cluster stability scores for microarray data in cancer studies

Mark Smolkin, Debashis Ghosh BMC Bioinformatics 2003, 4:36 (6 September 2003)

Research   Open Access Highly Accessed

Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering

Audrey P Gasch, Michael B Eisen Genome Biology 2002, 3:research0059-research0059.22 (10 October 2002)

A heuristically modified version of fuzzy k-means clustering has been used to identify overlapping clusters of yeast genes based on published gene-expression data following the response of yeast cells to environmental changes. A prevalent theme in the regulation of yeast gene expression seems to be the condition-specific coregulation of overlapping sets of genes.