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2020
[1]	Champion, M., Chiquet, J., Neuvial, P., Elati, M., Radvanyi, F. & Birmele, E.E. Identification of deregulated transcription factors involved in subtypes of cancers. In Proceedings of the 12th International Conference on Bioinformatics and Computational Biology, pages 1-10, 2020.
2019
[2]	Ambroise, C., Dehman, A., Neuvial, P., Rigaill, G. & Vialaneix, N. Adjacency-constrained hierarchical clustering of a band similarity matrix with application to Genomics. Algorithms for Molecular Biology, 14:22, BioMed Central, 2019.
[3]	Ambroise, C., Dehman, A., Neuvial, P., Rigaill, G. & Vialaneix, N. Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics. , 2019., (Poster).
2018
[4]	Sadacca, B., Hamy, A.S., Laurent, C., Gestraud, P., Bonsang-Kitzis, H., Pinheiro, A., Abecassis, J., Neuvial, P. & Reyal, F. New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panels (vol 7, 15126, 2016). Scientific Reports, 8, Nature Publishing Group, 2018., (Author Correction: New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panels).
[5]	Ambroise, C., Dehman, A., Koskas, M., Neuvial, P., Rigaill, G. & Vialaneix, N. Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics. In Journée Régionale de Bioinformatique et Biostatistique, Génopole Toulouse, 2018.
2017
[6]	Sadacca, B., Hamy-Petit, A.S., Laurent, C., Gestraud, P., Bonsang-Kitzis, H., Pinheiro, A., Abecassis, J., Neuvial, P. & Reyal, F. New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panels. Scientific Reports, 7(1):15126, Nature Publishing Group, 2017., (Author Correction : Sadacca, B., Hamy, A.-S., Laurent, C., Gestraud, P., Bonsang-Kitzis, H., Pinheiro, A., Abecassis, J., Neuvial, P., Reyal, F. (2018). New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panels (vol 7, 15126, 2016). Scientific Reports, 8, 1-1 https://www.nature.com/articles/s41598-018-36812-3 UT=WOS000453222300001).
2015
[7]	Picchetti, T., Chiquet, J., Elati, M., Neuvial, P., Nicolle, R. & Birmele, E.E. A model for gene deregulation detection using expression data. BMC Systems Biology, 9, BioMed Central, 2015.
[8]	Dehman, A., Ambroise, C. & Neuvial, P. Performance of a blockwise approach in variable selection using linkage disequilibrium information. BMC Bioinformatics, 16:14, BioMed Central, 2015.
[9]	Pierre-Jean, M., Rigaill, G. & Neuvial, P. Performance evaluation of DNA copy number segmentation methods. Briefings in Bioinformatics, 16(4):600-615, Oxford University Press (OUP), 2015.
[10]	Chambaz, A. & Neuvial, P. tmle.npvi: targeted, integrative search of associations between DNA copy number and gene expression, accounting for DNA methylation: Fig. 1.. Bioinformatics, 31(18):3054-6, Oxford University Press (OUP), 2015.
2014
[11]	Pierre-Jean, M., Rigaill, G. & Neuvial, P. Performance evaluation of DNA copy number segmentation methods. Briefings in Bioinformatics, 2014.
2013
[12]	Brito, I., Hupe, P., Neuvial, P. & Barillot, E. Stability-based comparison of class discovery methods for array-CGH profiles. PLoS One, 8(12):e81458, 2013.
[13]	Neuvial, P. Asymptotic Results on Adaptive False Discovery Rate Controlling Procedures Based on Kernel Estimators. Journal of Machine Learning Research, 14():1423\−1459, 2013.
[14]	Dehman, A., Ambroise, C. & Neuvial, P. Incorporating linkage disequilibrium blocks in Genome-Wide Association Studies. In JOBIM proceeding 2013, ():, , 2013.
[15]	Neuvial, P. Vers une médecine personnalisée grâce à la recherche en génomique. , 2013., (Variances Vol. 48 pp 31-33).
2012
[16]	Chambaz, A., Neuvial, P. & van der Laan, M. Estimation of a non-parametric variable importance measure of a continuous exposure. Electronic Journal of Statistics, 6():1059-1099, 2012.
[17]	Jacob, L., Neuvial, P. & Dudoit, S. More Power via Graph-Structured Tests for Differential Expression of Gene Networks. Annals of Applied Statistics, 6(2):561-600, 2012.
[18]	Neuvial, P. & Roquain, E. On False Discovery Rate thresholding for classification under sparsity. Annals of Statistics, 40(5):2572-2600, 2012.
[19]	Ortiz-Estevez, M., Aramburu, A., Bengtsson, H., Neuvial, P. & Rubio, A. CalMaTe: A Method and Software to Improve Allele-Specific Copy Number of SNP Arrays for Downstream Segmentation. Bioinformatics, 28(13):1793-1794, 2012.
2011
[20]	Olshen, A., Bengtsson, H., Neuvial, P., Spellman, P., Olshen, R. & Seshan, V. Parent-specific copy number in paired tumor-normal studies using circular binary segmentation. Bioinformatics, 27(15):2038-2046, 2011.
[21]	Neuvial, P., Bengtsson, H. & Speed, T. Handbook of Statistical Bioinformatics. , ():225-255, Springer, 2011.
[22]	Neuvial, P. Tests multiples en génomique. , 2011., (La gazette des mathématiciens No. 130 pp. 71-76).
2010
[23]	Bengtsson, H., Neuvial, P. & Speed, T. TumorBoost: Normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays. BMC bioinformatics, 11(1):245, 2010.
2009
[24]	Neuvial, P. & Bourguignon, P.Y. Problématiques statistiques à l'heure de la post-génomique. , 2009.
2008
[25]	Neuvial, P. Contributions à l'analyse statistique des données de puces à ADN. , 2008. (Theses)
[26]	Bollet, M., Servant, N., Neuvial, P., Decraene, C., Lebigot, I., Meyniel, J., De Rycke, Y., Savignoni, A., Rigaill, G., Hupe, P., Fourquet, A., Sigal-Zafrani, B., Barillot, E. & Thiery, J.P. High-resolution mapping of DNA breakpoints to define true recurrences among ipsilateral breast cancers. J Natl Cancer Inst, 100(1):48-58, 2008.
[27]	Neuvial, P. Asymptotic properties of false discovery rate controlling procedures under independence. Electron. J. Stat., 2():1065-1110, 2008.
2007
[28]	Elati, M., Neuvial, P., Bolotin-Fukuhara, M., Barillot, E., Radvanyi, F. & Rouveirol, C. LICORN: LearIng COoperative Regulation Networks. Bioinformatics, 23(18):2407-2414, 2007.
2006
[29]	La Rosa, P., Viara, E., Hupe, P., Pierron, G., Liva, S., Neuvial, P., Brito, I., Lair, S., Servant, N., Robine, N., Manie, E., Brennetot, C., Janoueix-Lerosey, I., Raynal, V., Gruel, N. & Others, O. VAMP: visualization and analysis of array-CGH, transcriptome and other molecular profiles. Bioinformatics, 22(17):2066-2073, 2006.
[30]	Liva, S., Hupe, P., Neuvial, P., Brito, I., Viara, E., La Rosa, P. & Barillot, E. CAPweb: a bioinformatics CGH array Analysis Platform. Nucleic Acids Res, 34(Web Server issue)(-):477-481, 2006.
[31]	Neuvial, P., Hupe, P., Brito, I., Liva, S., Manie, E., Brennetot, C., Radvanyi, F., Aurias, A. & Barillot, E. Spatial normalization of array-CGH data.. BMC Bioinformatics, 7(1):264, 2006.
2002
[32]	Elie, R., Frachot, A., Georges, P. & Neuvial, P. A Model of Prepayment for the French Residential Loan Market. , 2002.

Introduction to testing and to multiple testing (by xkcd) Another introduction (in French)

1. Asymptotic Results on Adaptive False Discovery Rate Controlling Procedures Based on Kernel Estimators (2013). Neuvial, P. Journal of Machine Learning Research Vol. 14, pp 1423−1459 [url] [pdf]
2. On False Discovery Rate thresholding for classification under sparsity (2012). Neuvial, P. and Roquain, E. Annals of Statistics Vol. 40, No. 5, pp. 2572-2600 [url] [pdf]
3. Asymptotic properties of false discovery rate controlling procedures under independence (2008). Neuvial, P. Electronic Journal of Statistics Vol. 2 pp. 1065–1110 [url] [pdf] [corrigendum]

Introductory slides (12/2011, Centrale ParisTech)

1. A performance evaluation framework of DNA copy number analysis methods in cancer studies; application to SNP array data segmentation methods (2015). Pierre-Jean, M. and Rigaill, G. and Neuvial, P. Briefings in Bioinformatics. Vol. 16 No. 4 pp. 600-615 [arxiv preprint]
2. Stability-based comparison of class discovery methods for array-CGH profiles (2013). Brito, I. and Hupé, P. and Neuvial, P. and Barillot, E. PLoS One Vol. 8 No. 12 pp. e81458 [paper]
3. CalMaTe: A Method and Software to Improve Allele-Specific Copy Number of SNP Arrays for Downstream Segmentation (2012). Ortiz-Estevez, M. and Aramburu, A. and Bengtsson, H. and Neuvial, P. and Rubio, A. Bioinformatics Vol. 28 No. 13 pp. 1793-1794 [paper] [R package: calmate] [R vignette]
4. Parent-specific copy number in paired tumor-normal studies using circular binary segmentation (2011). Olshen, A.B. et al. Bioinformatics Vol. 27 No. 15 pp. 2038-2046 [paper] [R package: PSCBS] [R vignette]
5. Statistical analysis of genotyping microarrays in cancer studies (2011). Neuvial, P. and Bengtsson, H. and Speed, T.P. Book chapter in Handbook of Statistical Bioinformatics. pp. 225-255. Springer. [paper]
6. TumorBoost: Normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays (2010). Bengtsson, H. and Neuvial, P. and Speed, T.P. BMC Bioinformatics Vol. 11 No. 1 pp. 245 [paper] [R vignette (high-level)] [R vignette (low-level)] Slides: [short version],[longer version]
7. CAPweb: a bioinformatics CGH array Analysis Platform (2006). Liva, S. et al. Nucleic Acids Res Vol. 34(Web Server issue) No. - pp. 477–481 [paper]
8. Spatial normalization of array-CGH data (2006). Neuvial, P. and Hupé, P. et al. BMC Bioinformatics Vol. 7 No. 1 pp. 264 [paper]
9. VAMP: visualization and analysis of array-CGH, transcriptome and other molecular profiles (2006). La Rosa, P. et al (2006). Bioinformatics Vol. 22 No. 17 pp. 2066–2073 [paper]

1. A model for gene deregulation detection using expression data BMC Systems Biology 9(Suppl 6):S6. Picchetti, T. and Chiquet, J. and Elati, M. and Neuvial, P. and Nicolle, R. and Birmelé, E. [Hal preprint]. Presented at the GIW/InCoB 2015 conference.
2. tmle.npvi: targeted, integrative search of associations between DNA copy number and gene expression, accounting for DNA methylation Bioinformatics (Application Note) Vol. 31(18):3054-6. Chambaz, A. and Neuvial, P. [Hal preprint][R package: tmle.npvi]
3. Performance of a Blockwise Approach in Variable Selection using Linkage Disequilibrium Information (2015), Dehman, A. and Ambroise, C. and Neuvial, P. BMC Bioinformatics [url][R package: BALD]
4. Estimation of a non-parametric variable importance measure of a continuous exposure (2012). Chambaz, A. and Neuvial, P. and van der Laan, M.J. Electronic journal of Statistics Vol. 6 pp 1059-1099 [paper] [extended abstract (in French)][slides (08/2012)]
5. More Power via Graph-Structured Tests for Differential Expression of Gene Networks (2012). Jacob, L. and Neuvial, P. and Dudoit, S. Annals of Applied Statistics Vol. 6 No. 2 pp 561–600 [url] [pdf] [Bioconductor package: DEGraph] [slides (01/2012)]
6. LICORN: LearIng COoperative Regulation Networks (2008). Elati, M. and Neuvial, P. and Bolotin-Fukuhara, M. and Barillot, E. and Radvanyi, F. and Rouveirol, C. Bioinformatics Vol. 23 No. 18 pp. 2407–2414. [paper]

1. Subtype and pathway specific responses to anticancer compounds in breast cancer (2012). Heiser, L. M. et al. PNAS [paper]
2. Integrated Genomic Analyses of Ovarian Carcinoma (2011). The Cancer Genome Atlas Network. Nature Vol. 474 No. 7353 pp. 609–615 [paper]
3. Identification of a CpG Island Methylator Phenotype that Defines a Distinct Subgroup of Glioma (2010). Noushmehr, H. et al. Cancer cell Vol. 17 No. 5 pp. 510–522 [paper]
4. High-resolution mapping of DNA breakpoints to define true recurrences among ipsilateral breast cancers (2008). Bollet, M. et al. J Natl Cancer Inst Vol. 100 No. 1 pp. 48–58 [paper]

1. Vers une médecine personnalisée grâce à la recherche en génomique (2013). Neuvial, P. Variances Vol. 48 pp 31–33 [paper]
2. Tests multiples en génomique (2011). Neuvial, P. La gazette des mathématiciens No. 130 pp. 71–76 [paper]
3. Problématiques statistiques à l'heure de la post-génomique (2009). Neuvial, P. and Bourguignon, P.-Y. Variances Vol. 35 pp. 56–60 [paper]

• Guillermo Durand (2015-): “Multiple testing for structured biological data”. Co-supervised with Etienne Roquain. Funded by Université Paris 6.
• Benjamin Sadacca (2013-): “Tumoral microenvironment and treatment response in breast cancers”. Co-supervised with Fabien Reyal. Funded by Institut Curie.
• Morgane Pierre-Jean (2013-): “Statistical methods for the analysis of structured genomic data”. Funded by École doctorale GAO, University of Évry.
• Alia Dehman (2012-2015/12/9): “Structured sparse regression for Genome-Wide Association Studies”. Co-supervised with Christophe Ambroise. Funded by École doctorale GAO, University of Évry.

2015-2016

• Programming for Genomics. Université d'Evry, Master 2 Ingénierie Statistique pour la Génomique (ISG)
• Statistique en grande dimension et génomique: segmentation de données de nombres de copies d'ADN en cancérologie. Formation Data science pour l'actuariat à l'Institut des Actuaires, décembre 2015.

2014-2015 (in French)

• Démarche Statistique I. Université d'Evry, Master 1 mention SGO
• Analyse statistique des données génomiques (a.k.a. “ASBDE”). Université Paris Sud, Master 2 BIBS
• Introduction aux méthodes statistiques en biologie moléculaire. ENSAE ParisTech, troisième année. Avec Catherine Matias.
  

(essentially driven by the contributions of Henrik Bengtsson)

• http://aroma-project.org: An open-source R framework for your microarray analysis, by Henrik Bengtsson et al.
• R package BALD
• R package jointseg
• R package tmle.npvi

Other software from LaMME

• Henrik Bengtsson, University of California at San Francisco, Epidemiology & Biostatistics.
• Antoine Chambaz, Université Paris Ouest Nanterre, Modal'X.
• Laurent Jacob, Laboratoire de Biométrie et Biologie Évolutive, Lyon.
• Etienne Roquain, Université Pierre et Marie Curie, LPMA.

my .screenrc

verbose on

# visual bell
vbell on

# startup message
startup_message off

# increase default scrollback
defscrollback 10000

# customization of the screen window
caption always "%{wk}%h %=%c"
hardstatus alwayslastline "%{=b}%{G}Screen(s): %{w}%-Lw%n%f* %t%{-}%{w}%+Lw%=%{G}Connected \
to %H"


# default programs
screen -t bash
screen -t emacs emacs
screen -t R R
screen -t htop htop