| Small-sample precision of ROC-related estimates B Hanczar, J Hua, C Sima, J Weinstein, M Bittner, ER Dougherty Bioinformatics 26 (6), 822-830, 2010 | 370 | 2010 |
| Analysis of feature selection stability on high dimension and small sample data D Dernoncourt, B Hanczar, JD Zucker Computational statistics & data analysis 71, 681-693, 2014 | 128 | 2014 |
| Accuracy-rejection curves (ARCs) for comparing classification methods with a reject option MSA Nadeem, JD Zucker, B Hanczar Machine Learning in Systems Biology, 65-81, 2009 | 119 | 2009 |
| Needle and surgical biopsy techniques differentially affect adipose tissue gene expression profiles DM Mutch, J Tordjman, V Pelloux, B Hanczar, C Henegar, C Poitou, ... The American journal of clinical nutrition 89 (1), 51-57, 2009 | 106 | 2009 |
| Classification with reject option in gene expression data B Hanczar, ER Dougherty Bioinformatics 24 (17), 1889-1895, 2008 | 88 | 2008 |
| Improving classification of microarray data using prototype-based feature selection B Hanczar, M Courtine, A Benis, C Hennegar, K Clément, JD Zucker ACM SIGKDD Explorations Newsletter 5 (2), 23-30, 2003 | 75 | 2003 |
| Decorrelation of the true and estimated classifier errors in high-dimensional settings B Hanczar, J Hua, ER Dougherty EURASIP journal on Bioinformatics and Systems Biology 2007, 1-12, 2007 | 69 | 2007 |
| Performance of error estimators for classification ER Dougherty, C Sima, B Hanczar, UM Braga-Neto Current Bioinformatics 5 (1), 53-67, 2010 | 66 | 2010 |
| In Vivo Epinephrine-Mediated Regulation of Gene Expression in Human Skeletal Muscle N Viguerie, K Clement, P Barbe, M Courtine, A Benis, D Larrouy, ... The Journal of Clinical Endocrinology & Metabolism 89 (5), 2000-2014, 2004 | 66 | 2004 |
| Ensemble methods for biclustering tasks B Hanczar, M Nadif Pattern Recognition 45 (11), 3938-3949, 2012 | 57 | 2012 |
| Using the bagging approach for biclustering of gene expression data B Hanczar, M Nadif Neurocomputing 74 (10), 1595-1605, 2011 | 52 | 2011 |
| Interpretable and accurate prediction models for metagenomics data E Prifti, Y Chevaleyre, B Hanczar, E Belda, A Danchin, K Clément, ... GigaScience 9 (3), giaa010, 2020 | 32 | 2020 |
| Feature construction from synergic pairs to improve microarray-based classification B Hanczar, JD Zucker, C Henegar, L Saitta Bioinformatics 23 (21), 2866-2872, 2007 | 30 | 2007 |
| Biological interpretation of deep neural network for phenotype prediction based on gene expression B Hanczar, F Zehraoui, T Issa, M Arles BMC bioinformatics 21, 1-18, 2020 | 27 | 2020 |
| On the comparison of classifiers for microarray data B Hanczar, ER Dougherty Current Bioinformatics 5 (1), 29-39, 2010 | 26 | 2010 |
| Deep GONet: self-explainable deep neural network based on Gene Ontology for phenotype prediction from gene expression data V Bourgeais, F Zehraoui, M Ben Hamdoune, B Hanczar BMC bioinformatics 22 (10), 1-25, 2021 | 22 | 2021 |
| Combination of one-class support vector machines for classification with reject option B Hanczar, M Sebag Machine Learning and Knowledge Discovery in Databases: European Conference …, 2014 | 21 | 2014 |
| Performance visualization spaces for classification with rejection option B Hanczar Pattern Recognition 96, 106984, 2019 | 20 | 2019 |
| Microarray profiling of human white adipose tissue after exogenous leptin injection S Taleb, R Van Haaften, C Henegar, C Hukshorn, R Cancello, V Pelloux, ... European journal of clinical investigation 36 (3), 153-163, 2006 | 20 | 2006 |
| The reliability of estimated confidence intervals for classification error rates when only a single sample is available B Hanczar, ER Dougherty Pattern Recognition 46 (3), 1067-1077, 2013 | 17 | 2013 |
Jean-Daniel ZuckerSenior Researcher, UMMISCO, IRD/Sorbonne University, FranceVerified email at ird.fr
