International Journals

  1. Prifti E; Chevaleyre Y; Hanczar B; Belda E; Danchin A; Clément K; Zucker J. Interpretable and accurate prediction models for metagenomics data. Gigascience, (2020)
  2. Blaise Hanczar. Performance visualization spaces for classification with rejection option. Pattern Recognition, vol. 96, p. 106984. (2019).
  3. Blaise Hanczar, Mathieu Henriette, Toky Ratovomanana, Farida Zehraoui. Phenotypes Prediction from Gene Expression Data with Deep Multilayer Perceptron and Unsupervised Pre-training. International Journal of Bioscience, Biochemistry and Bioinformatics 8(2): 125-131. (2018)
  4. Blaise HanczarJean-Daniel ZuckerAn approach to optimizing abstaining area for small sample data classification. Expert Syst. Appl. 95153-161 (2017)
  5. David Dernoncourt, Blaise Hanczar, Jean-Daniel Zucker. Analysis of feature selection stability on high dimension and small sample data. Computational Statistics & Data Analysis. 71:681-693 (2014).
  6. Blaise Hanczar, Edward R. Dougherty. The reliability of estimated confidence intervals for classification error rates when only a single sample is available. Pattern Recognition, 46(3):1067-1077 (2012).
  7. Blaise Hanczar,  Mohamed Nadif. Ensemble method for biclustering tasks. Pattern recognition. 45(11):3938-3949 (2012)
  8. Blaise Hanczar, Avner Bar-Hen. A new measure of classifier performance for gene expression data. IEEE Transactions on Computational Biology and Bioinformatics, 9(5):1379-1386 (2012)
  9. Blaise Hanczar, Mohamed Nadif. Using the bagging approach for biclustering of gene expression data. Neurocomputing 74(10): 1595-1605 (2011)
  10. Blaise Hanczar, Jianping Hua, Chao Sima, John Weinstein, Michael Bittner, Edward R. Dougherty. Small-sample precision of ROC-related estimates. Bioinformatics, 26:822-830 (2010)
  11. Blaise Hanczar, Edward R. Dougherty. On the Comparison of Classifiers for Microarray Data. Current Bioinformatics, 5(1):29-39. (2010)
  12. Dougherty ER, Sima C, Hua J, Hanczar B, Braga-Neto UM. Performance of Error Estimators for Classification. Current Bioinformatics. 5(1):53-67. (2010)
  13. Blaise Hanczar, Corneliu Henegar, Jean-Daniel Zucker. Exploring interaction measures to identify informative pairs of genes. International Journal of Bioinformatics Research and Applications. 6(6): 628-642 (2010)
  14. Mutch DM, Tordjman J, Pelloux V, Hanczar B, Henegar C, Poitou C, Veyrie N, Zucker JD, Clément K. Needle and surgical biopsy techniques differentially affect adipose tissue gene expression profiles. Am. Journal Clinical Nutrition 89(1):51-57. (2009).
  15. Blaise Hanczar, Edward R. Dougherty. Classification with reject option in gene expression data. Bioinformatics. 4(17):1889-1895 (2008).
  16. Blaise Hanczar, Jianping Hua, Edward Dougherty. Decorrelation of the True and Estimated Classifier Errors in High-dimensional Settings. EURASIP Journal on Bioinformatics and Systems Biology , Article ID 38473. (2007).
  17. Blaise Hanczar, Jean-Daniel Zucker, Corneliu Hennegar, Lorenza Saitta. Feature construction from synergic pairs to improve microarray-based classification. Bioinformatics.23(21):2866-2872.(2007).
  18. Taleb S, Van Haaften R, Henegar C, Hukshorn C, Cancello R, Pelloux V, Hanczar B, Viguerie N, Langin D, Evelo C, Zucker J, Clement K, Saris WH. Microarray profiling of human white adipose tissue after exogenous leptin injection. Eur. Journal Clinical Investigation 36(3):153-163. (2006).
  19. Viguerie N, Clément K, Barbe P, Courtine M, Larrouy D, Hanczar B, Bénis A, Poitou C, Khalfallah Y, Barsh G, Thalamas C, Zucker JD, Langin D. Pangenomic cDNA microarray profiling of human skeletal muscle gene expression during epinephrine infusion. J Clin Endoc Metab 89:2000-2014 (2004).
  20. Blaise Hanczar, Mélanie Courtine, Arriel Benis, Corneliu Hennegar, Karine Clement, Jean-Daniel Zucker. Improving classification of microarray data using prototype-based feature selection. SIGKDD Explorations 5(2): 23-30 (2003).

International Conferences

  1. Elies Gherbi, Blaise Hanczar, Jean-Christophe Janodet and Witold Klaudel. An Encoding Adversarial Network for Anomaly Detection. Accepted in ACML (2019).
  2. Mathieu Clertant, Yann Chevaleyre, Blaise Hanczar, Natalya Sokolovsha. Interpretable Cascade Classifiers with Abstention. In The 22nd International Conference on Artificial Intelligence and Statistics AISTAT, 2312-2320. (2019)
  3. Blaise Hanczar, Mohamed Nadif. Controlling and visualizing the precision-recall trade-off for external performance indice. European Conference on Machine Learning ECML Dublin, Ireland. (2018)
  4. Blaise Hanczar, Mathieu Henriette, Toky Rana , Farida Zehraoui. Phenotypes prediction from gene expression data with deep multilayer perceptron and unsupervised pre-training. 6th International Conference on Computer Engineering and Bioinformatics ICCEB. (2017) 
  5. Blaise Hanczar, Avner Bar-Hen. Controlling the Cost of Prediction in using a Cascade of Reject Classifiers for Personalized Medicine. 9th International Joint Conference on Biomedical Engineering Systems and Technologies. 3: 42-50 (2016).
  6. Blaise Hanczar, Michèle Sebag. Combination of one-clas svm for classification with reject option. European Conference on Machine Learning ECML. Nancy, France. (2014)
  7. Blaise Hanczar, Mohamed Nadif . Unsupervised consensus functions applied to ensemble biclustering. International Conference on Pattern Recognition Applications and Methods ICPRAM. Angers, France, (2014)
  8. David Dernoncourt, Blaise Hanczar, Jean-Daniel Zucker. Stability of Ensemble Feature Selection on High-Dimension and Low-Sample Size Data: Influence of the Aggregation Method. International Conference on Pattern Recognition Applications and Methods ICPRAM. Angers, France, (2014)
  9. Blaise Hanczar, Mohamed Nadif. Precision Recall space to correct external indices for biclustering. International Conference on Machine Learning ICML. Atlanta, USA.  (2013) 
  10. David Dernoncourt, Blaise Hanczar, Jean-Daniel Zucker. Experimental Analysis of Feature Selection Stability for High-Dimension and Low-Sample Size Gene Expression Classification Task. IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE). (2012)
  11. Blaise Hanczar, Mohamed Nadif. Improving the Biological Relevance of Biclustering for Microarray Data in Using Ensemble Methods. 2nd International Workshop on Biological Knowledge Discovery and Data Mining BIOKDD: 413-417. Toulouse, France (2011)
  12. David Dernoncourt, Blaise Hanczar, Jean-Daniel Zucker. An Empirical Analysis of Markov Blanket Filters for Feature Selection on Microarray Data. International Workshop on Machine Learning in Systems Biology MLSB. (2011)
  13. Blaise Hanczar, Mohamed Nadif. Bagging for Biclustering: Application to Microarray Data. European Conference on Machine Learning ECML (1) 2010: 490-505. Barcelona, Spain. (2010)
  14. Blaise Hanczar, Mohamed Nadif. Bagged Biclustering for Microarray Data. European Conference on Artificial Intelligence ECAI 2010: 1131-1132. Lisbon, Portugal. (2010)
  15. Nadeem MS., Zucker JD., Hanczar B. Accuracy-Rejection Curves (ARCs) for Comparison of Classification Methods with Reject Option. International Workshop on Machine Learning in Systems Biology. MLSB. Ljubljana, Slovenia. (2009)
  16. Blaise Hanczar, Jianping Hua, Edward Dougherty. Is There Correlation Between the Estimated and True Classification Errors in Small-Sample Settings? IEEE Statistical Signal Processing SSP. Madison, USA. (2007).
  17. Blaise Hanczar. Combining feature selection and feature construction to improve concept learning for high dimensional data. Symposium on Abstraction Reformulation and Approximation SARA 261-273. (2005)
  18. Blaise Hanczar, Mélanie Courtine, Ariel Benis, Corneliu Hennegar, Karine Clément, Jean-daniel Zucker. ProGene: A prototype-gene based dimension reduction approach for microarray data classification. Critical assement on Microarray Data Analsyis CAMDA, (2003).

National Conferences

  1. Elies Gherbi, Blaise Hanczar, Jean-Christophe Janodet and Witold Klaudel. Construction d'espace latent pour la détection d'anomalies par apprentissage adversarial. Conférence Francophone d’Apprentissage Automatique CAp 2019, Toulouse, France, (2019).
  2. Mona Mayouf, Amel Hamrioui, Insaf Setirtra, Farida Zehraoui, Blaise Hanczar. La classification automatique des images histologiques du cancer su sein : entre les méthodes basées segmentation et les méthodes basées RNC. Société Francophone de Classification SFC18. Paris, France. (2018).
  3. Blaise Hanczar, Mohamed Nadif. Le compromis précision-rappel dans l'évaluation des performances. Extraction et Gestion de Connaissances EGC15. Luxembourg. (2015) 
  4. Blaise Hanczar, Mohamed Nadif. Different approaches of consensus functions in the context of ensemble methods for biclustering. Conférence Francophone sur l'apprentissage automatique Cap13. Lille, France. (2013)
  5. Blaise Hanczar,  Mohamed Nadif. Correction des indices de qualité pour le biclustering. Société Francophone de Classification SFC12. Marseille, France. (2012)
  6. David Dernoncourt, Blaise Hanczar, Jean-Daniel Zucker. Évolution de la stabilité de la sélection de variables en fonction de la taille d’échantillon et de la dimension. Conférence Francophone d’Apprentissage Automatique CAp 2012, Nancy, France, (2012).
  7. Nadeem M.S., Zucker J.D. and Hanczar B. Combining Rejection Regions for Microarray-based Classification. Conférence Francophone d’Apprentissage Automatique CAp 2011, Chambéry, France, 377-392, (2011)
  8. Blaise Hanczar, Mohamed Nadif. Improving biclustering perfomance with the bagging approach for microarray data analysis. Conférence Francophone sur l'apprentissage automatique Cap2010. Clermond-Ferrand, France. (2010)
  9. Blaise Hanczar. Reliability of error estimation in small-sample high-dimensional classification problems. Société Francophone de Classification SFC09. Grenoble, France. (2009)
  10. Blaise Hanczar, Jean-Daniel Zucker. Analyse de données d’expression par les réseaux d’interaction d’information. Réseaux d'interactions : analyse, modélisation et simulation RIAMS06. (2006).
  11. Blaise Hanczar, Jean-Daniel Zucker. Exploiter l'information mutuelle inter-gènes pour réduire la dimension des données biopuces: une approche basée sur la construction automatique d'attributs. Conférence Francophone d'Apprentissage: 247-262. (2005)
  12. Mohamed-Ramzi Temanni, Blaise Hanczar, Jean-Daniel Zucker. Combinaison des données d’expressions génique et des données cliniques pour améliorer la qualité de la prédiction de la survie à 5 ans de patients atteints de cancer. Journée Francophone d'Informatique Médicale JFIM 2005.

    Colloques, Workshops et Séminaires Invités

    • 01/2020, Démonstrateur Metagenopolis, INRA
    • 11/2019, Colloque Génomique Numérique (Genopole)
    • 10/2019, Equipe MaiAGE, INRA
    • 06/2019, Journée PEPI Ingénierie Bio Informatique et Statistique pour les données haut-débit (INRA)
    • 05/2019, Journées scientifiques du Cancéropole  Nord-Ouest.
    • 04/2019, Laboratoire de recherche en Information Equipe Bioinfo, Université Paris-sud (LRI)
    • 12/2018, Colloque Évry-Sénart Sciences et Innovation (ESSI)
    • 10/2018, Workshop « deep learing et génomique » INRA   
    • 06/2018, Centre National de Recherche en Génomique Humaine (CNRGH)
    • 10/2017, Workshop PREDICT Psay-CompBio 
    • 01/2017, Centre d’étude et de recherche en informatique et communication, CNAM (CEDRIC) 
    • 12/2016, Centre Hospitalier Sud Francilien, service rhumatologie.
    • 04/2016, Colloque Evry Science et Innovation « Santé et Numérique »
    • 02/2016, Laboratoire Mathématique et Modélisation (LaMME)
    • 03/2015, laboratoire Informatique, Biologie Intégrative et Système Complexe (IBISC). 
    • 07/2014, Laboratoire d'informatique de l'université Paris Descartes (LIPADE)
    • 12/2013, Laboratoire de recherche en Information Equipe TAO, Université Paris-Sud (LRI)
    • 12/2013, Laboratoire d'informatique Paris Nord, Université Paris-Nord (LIPN)
    • 09/2010, Inserm U872 Equipe Nutriomique
    • 03/2009, ECAIS, IUT Paris Descartes
    • 05/2008, Inserm U872 Equipe Nutriomique
    • 12/2007, Genomics Signal Processing Lab, Texas A&M university.
    • 02/2007, Translational Genomics Research Institute (TGen).
    • 12/2006, Genomics Signal Processing Lab, Texas A&M university.
    • 06/2006, Hopital Hotel-Dieu, Equipe Nutriomique 
    • 06/2005, Laboratoire de recherche en Information Equipe TAO, Université Paris-Sud (LRI)
    • 01/2004, Hopital Hotel-Dieu, Equipe Nutriomique 
    • 06/2003, Laboratoire Servier
    • 02/2003, Laboratoire Bioinformatique et informatique médical, Univ Paris Nord (LimBio)