BMC Bioinformatics

official impact factor 3.03

Open Access

A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets

Carmen Lai*, Marcel JT Reinders, Laura J van't Veer and Lodewyk FA Wessels

BMC Bioinformatics 2006, 7:235 doi:10.1186/1471-2105-7-235

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BioMed Central: 9 citations

Research article   Open Access Highly Accessed

Microarray-based cancer prediction using single genes

Xiaosheng Wang, Richard Simon BMC Bioinformatics 2011, 12:391 (7 October 2011)

Research article   Open Access Highly Accessed

Top scoring pairs for feature selection in machine learning and applications to cancer outcome prediction

Ping Shi, Surajit Ray, Qifu Zhu, Mark A Kon BMC Bioinformatics 2011, 12:375 (23 September 2011)

Research article   Open Access Highly Accessed

Effect of training-sample size and classification difficulty on the accuracy of genomic predictors

Vlad Popovici, Weijie Chen, Brandon D Gallas, Christos Hatzis, Weiwei Shi, Frank W Samuelson, Yuri Nikolsky, Marina Tsyganova, Alex Ishkin, Tatiana Nikolskaya, Kenneth R Hess, Vicente Valero, Daniel Booser, Mauro Delorenzi, Gabriel N Hortobagyi, Leming Shi, W Fraser Symmans, Lajos Pusztai Breast Cancer Research 2010, 12:R5 (11 January 2010)

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A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability

Herman MJ Sontrop, Perry D Moerland, René van den Ham, Marcel JT Reinders, Wim FJ Verhaegh BMC Bioinformatics 2009, 10:389 (26 November 2009)

Research   Open Access Highly Accessed

A kernel-based integration of genome-wide data for clinical decision support

Anneleen Daemen, Olivier Gevaert, Fabian Ojeda, Annelies Debucquoy, Johan AK Suykens, Christine Sempoux, Jean-Pascal Machiels, Karin Haustermans, Bart De Moor Genome Medicine 2009, 1:39 (3 April 2009)

A kernel-based supervised classification algorithm allows the integration of genome-wide data from different sources and improves the prediction of clinical outcomes from cancer data sets.

Methodology article   Open Access Highly Accessed

Iterative Bayesian Model Averaging: a method for the application of survival analysis to high-dimensional microarray data

Amalia Annest, Roger E Bumgarner, Adrian E Raftery, Ka Yee Yeung BMC Bioinformatics 2009, 10:72 (26 February 2009)

Proceedings   Open Access

Very Important Pool (VIP) genes – an application for microarray-based molecular signatures

Zhenqiang Su, Huixiao Hong, Hong Fang, Leming Shi, Roger Perkins, Weida Tong BMC Bioinformatics 2008, 9(Suppl 9):S9 (12 August 2008)

Methodology article   Open Access Highly Accessed

Prediction of the outcome of preoperative chemotherapy in breast cancer using DNA probes that provide information on both complete and incomplete responses

René Natowicz, Roberto Incitti, Euler Horta, Benoît Charles, Philippe Guinot, Kai Yan, Charles Coutant, Fabrice Andre, Lajos Pusztai, Roman Rouzier BMC Bioinformatics 2008, 9:149 (15 March 2008)

Methodology article   Open Access

Gene selection for classification of microarray data based on the Bayes error

Ji-Gang Zhang, Hong-Wen Deng BMC Bioinformatics 2007, 8:370 (3 October 2007)