Blaise Hanczar
Blaise Hanczar
Professor, Université Paris-Saclay (Univ. Evry)
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Cited by
Cited by
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
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
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
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
Classification with reject option in gene expression data
B Hanczar, ER Dougherty
Bioinformatics 24 (17), 1889-1895, 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
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
Performance of error estimators for classification
ER Dougherty, C Sima, B Hanczar, UM Braga-Neto
Current Bioinformatics 5 (1), 53-67, 2010
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
Ensemble methods for biclustering tasks
B Hanczar, M Nadif
Pattern Recognition 45 (11), 3938-3949, 2012
Using the bagging approach for biclustering of gene expression data
B Hanczar, M Nadif
Neurocomputing 74 (10), 1595-1605, 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
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, 1-25, 2021
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
Feature construction from synergic pairs to improve microarray-based classification
B Hanczar, JD Zucker, C Henegar, L Saitta
Bioinformatics 23 (21), 2866-2872, 2007
On the comparison of classifiers for microarray data
B Hanczar, ER Dougherty
Current Bioinformatics 5 (1), 29-39, 2010
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
Performance visualization spaces for classification with rejection option
B Hanczar
Pattern Recognition 96, 106984, 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
Assessment of deep learning and transfer learning for cancer prediction based on gene expression data
B Hanczar, V Bourgeais, F Zehraoui
BMC bioinformatics 23 (1), 262, 2022
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