G. Nuel is a senior CNRS researcher of the Institute of Mathematics (INSMI) working in Laboratory of Probability and Stochastic Models (LPMA) at Université Pierre et Marie Curie (UPMC), Sorbonne Universités. Throughout his career, G. Nuel has developed a genuine interest for biomedical applications in probability and statistics based on his strong theoretical background in mathematics. He is an expert in computational statistics (simulations, the expectation-maximization algorithm, Markov chain Monte Carlo techniques, etc.) and models with latent variables (Markov chains, hidden Markov models, Bayesian networks, etc.). He has a great interest for applications in bioinformatics, statistical genetics, cancer epidemiology, tropical diseases, and clinical research.

Contact Information

Pr. Gregory Nuel
LPMA, CNRS 7599
UPMC, Case courrier 188
Office 16-26.122

4 place Jussieu,
75005 Paris - France

nuel@math.cnrs.fr

Background and Positions

  • Since 2015: Member of the National Epidemiology Board of LNCC
  • Since 2014: Member of the CSS2 of the INSERM
  • Since 2013: DR (Directeur de Recherche) CNRS at UPMC
  • 2007-2013: CR (Chargé de Recherche) CNRS at UPD
  • 2006: HDR in Mathematics (Habilitation à Diriger des Recherches)
  • 2005-2007: Contractual CNRS Researcher at the University of Evry
  • 2001-2007: Assistant Professor at the University of Evry
  • 1998-2001: PhD. in Mathematics (University of Evry)
  • 1996-1998 : Master’s Degree in Maths (University of Orsay)
  • 1996-1997 : French Agregation of Mathematics
  • 1991-1996 : Undergraduate and Graduate Degrees in Mathematics (University of Orsay)

Scientific Summary

  • 15 Years of Academic Experience
  • 38 Peer-Reviewed Articles (H-index of 17)
  • More than 50 Communications in International Conferences
  • Mentoring of 35 Students and Postdocs
  • More than €1M of Research Grants Obtained/Managed
  • National and International Academic Teaching (> 60h/year)
  • Regular Reviewer for many Academic Journals
  • Consulting in Biostatistics for the Pharmaceutical Industry
  • Main Methodological Themes of Interest:
    • Probabilistic Graphical Models (Bayesian Networks, HMMs)
    • Computational Statistics (EM algorithm, MCMC)
    • Generalized Linear Models and Survival Analysis


Projects

Sequences in Bioinformatics

This is our historical research theme. The objective is to develop statistical methods dealing with sequence data in bioinformatics: DNA, RNA reads, protein sequences, etc. Most develop methods are based on Markov models (homogeneous or not).

In this context, we are are interested in:

  • alignment (pairwise, multiple) of sequences
  • distribution of motifs in random sequences
  • structural alphabet and 3D structure encoding
  • local score statistics

Main partners involved:

  • A.-C. Camproux (MTi, University Paris Diderot, Paris, France)
  • I. Allam (MTi, University Paris Diderot, Paris, France)
  • S. Mercier (University Toulouse 2, Toulouse, France)
  • V. Delos (CNRS, University of Bordeaux, Bordeaux, France)

Grant: Sorbonne Paris Cité (2013-2016, €200K).

Survival and Genetics

We focus here on the genetic factors in important age-dependent diseases like cancer, diabetes or rare genetic diseases. The challenge is to combine state-of-art survival analysis methods in the context of genetic dependence in (possibly large) pedigrees.

In this context, we are are interested in:

  • belief propagation in pedigrees
  • detection and estimation of cohort-effects
  • development of cure models
  • clinical cancer genetics

Main partners involved:

  • D. Stoppa-Lyonnet (Institut Curie, Paris, France)
  • A. de Pauw (Institut Curie, Paris, France)
  • F. Alarcon (MAP5, University Paris Descartes, Paris, France)
  • O. Bouaziz (MAP5, University Paris Descartes, Paris, France)
  • T. Belaribi (LPMA, UPMC, Paris, France)

Grants: INSERM/IRESP DECURION (2013-2016, €120K), LNCC PhD grant (2013-2016, €100K).

Causality and Gene Expression

We focus here on the inference of causal relationships between genes in system biology using both observational and interventional experiments. Our main model is based on Gaussian Bayesian networks.

In this context, we are are interested in:

  • inference of causal (partial) ordering
  • development of marginal causal tests
  • modelisation of intervention experiments
  • biomedical applications (agronomy, cancer, rare genetic disease, etc.)

Main partners involved:

  • F. Jaffrézic (INRA, Jouy-en-Josas, France)
  • A. Rau (INRA, Jouy-en-Josas, France)
  • G. Monneret (LPMA, UPMC, Paris, France)
  • A. Hartmann (University of Oldenburg, Germany)

Grants: PhD grant ED386 (2014-2017, €90K), INRA (2015-2017, €40K).

Genetic Epidemiology

In Genome-Wide Association Studies (GWAS) we study the relation between a phenotype (usually a binary trait, ex: affected/non-affected by a disease) and high density genotypes (ex: 500,000 SNPs).

In this context, we are are interested in:

  • realistic H1 simulations
  • new association statistics
  • regularized regressions
  • GWAS with longitudinal responses

Main partners involved:

  • C. Sinoquet (University of Nantes, Nantes, France)
  • F. Alarcon (MAP5, University Paris Descartes, Paris, France)
  • V. Perduca (MAP5, University Paris Descartes, Paris, France)
  • F. Frommlet (University of Vienna, Vienna, Austria)

Grant: ANR SAMOGWAS (2013-2017, €20K).



Ciguatera

Ciguatera Fish Poisoning (CFP) is a foodborne illness caused by the presence of a neuro-toxin (Ciguatera Toxin - CTX) in certain reef fishes. The CTX is produced by micro-algaes like Gambierdiscus toxicus and is present in many tropical sea environment (French Polynesia, The Caribbean).

In this context, we are are interested in:

  • the development of fish specific CTX tests
  • system biology of CTX (long-term) exposure
  • the epidemiology of the disease in local populations

Main partners involved:

  • M. Chinain (ILM, Tahiti, French Polynesia)
  • Y. Bottein (IAEA, Monaco)
  • F. Letourneur (INSERM, Cochin Hospital, Paris, France)
  • J.-P. Jais (Necker Hospital, Univ. Paris Descartes, Paris, France)

Grant: French Polynesia Territory (2016-2019, €50K).

HMMs for Audio Signals

This exciting new project with musicologists and bioacousticians aim developing hidden Markov models for the analysis of audio signals. Applications range from ethno-musicology (Madagascar, India) to sea mammals communications (whales, dolphins).

In this context, we are are interested in:

  • automatic transcription of music
  • high-level unsupervised harmonic analysis
  • multi-level musical homology
  • asynchronous HMMs and semi-Markov models

Main partners involved:

  • O. Adam (UPMC, Paris, France)
  • D. Cazau (ENSTA Bretagne, Brest, France)



Teaching

Courses

EM Algorithm

In 1977, Dempster, Lair and Rubin introduced the Expectation-Maximization algorithm providing a general approach for the likelihood maximization of any model with latent variables. The purpose of this course is to understand this incredibly useful algorithm and to be able to apply/implement it. We first start by considering the Gaussian mixture model and introduce the EM algorithm as natural extension of its stochastic form. We then consider various application example where the EM algorithm applies: various mixtures, censoring, generalized linear models, linear mixed models. The second part of the course is dedicated to individual project: each student chooses any of the 40,000 paper citing the Dempster et al. (1977) and understand/implement the EM part of the paper.

Prerequisite: L3 degree in probability/statistics, basic programming skills.

Locations: Applied Maths M1, Univ. Paris Descartes, 2009-2012; Applied Maths M2, Univ. Paris Descartes, since 2012.

Didactic page: Gaussian mixture

Belief Propagation in Bayesian Networks

Graphical networks are very general directed probabilistic graphical models which are widely used both in theoretical and applied contexts. Important particular cases include Markov and hidden Markov models. In this course, we focus on the algorithmic computation of joint and conditional distributions in Bayesian networks using the belief propagation algorithm. We first introduce the notion of Bayesian network, potential and evidence. Then we intuitively introduce junction trees by considering variable elimination. Finally, we define formally the concept of message propagation and apply it to various models. The whole course is illustrated with many practical examples (from constrained Markov chains to genetic pedigrees) and we also provide a educational R library allowing to implement/experiment Bayesian networks and message passing algorithms.

Prerequisite: L3 degree in probability/statistics, basic programming skills.

Locations: Biostatistics M2, Univ. Paris Descartes since 2010; Applied Maths M2 CIPMA (Cotonou, Benin), 2013; Probability M2, UPMC, since 2014.

Sequences in Bioinformatics

A large proportion of the data of interest in bioinformatics are sequences (DNA, short reads, proteins, etc). The purpose of the course is to provide basic knowledge on the statistical modeling of sequences as well as introducing the notion of empirical significance of a bioinformatic observation. We start by recalling the notion of statistical control of an experiment, null hypothesis, and p-value. We then present independent and Markov homogeneous models for sequences, parameter training, and model selection. Finally, we apply our empirical approach to various typical bioinformatics problems: motifs (PSSM or regex), local score of one sequence, alignment.

Prerequisite: L3 degree in bioinformatics, basic programming skills.

Locations: Bioinformatic M1, Univ. Paris Diderot, since 2010; International Bioinformatics M2, UPMC, since 2013.

Introduction to HMMs

Hidden Markov models are the simplest possible extension of mixture models which relax the independence assumption of the latent classes by introducing a Markov dependency for the hidden process. HMMs are used in many applications: detection of homogenous regions in bioinformatics, analysis of time series, linguistic, automatic audio transcription, etc. In this course, we use the Gaussian mixture as a base model which is then extended to its corresponding HMM. We present the Forward/Backward algorithm (particular case of belief propagation in Bayesian network) and show how to use it to obtain various posterior distributions of interest. Finally we quickly present EM-based training of HMMs. The whole course is illustrated with simulations and R illustrations.

Prerequisite: L3 degree in science, basic programming skills.

Locations: Spring Meeting in Probability (Tunis, Tunisia), 2015; Bioinformatic M2, Univ. Paris Diderot, since 2015.

Some support slides:

Mentoring

Gilles Monneret (PhD) Development of Causal Models and Applications to the Inference of Genes Regulation Networks (started in 2014)

In collaboration with A. Rau and F. Jaffrezic (INRA, Jouy-en-Josas, France)

Funding (3 years): Université Pierre et Marie Curie

Ikram Allam (PhD) Developpement d'un alphabet structural pour l'analyse des structures 3D des protéines (started in 2013)

In collaboration with A.-C. Camproux (Université Paris Diderot, Paris, France)

Funding (3 years): Sorbonne Paris-Cité (SA-Flex project)

Eric Adjakossa (PhD) Analyse longitudinale multivariée et application à des données immunologiques sur le paludisme (started in 2013)

In collaboration with M. N. Hounkonnou (CIPMA, Cotonou, Benin)

Funding (3 years): SCAC and Institut pour la Recherche et le Développement (IRD)

Tihninan Belaribi (PhD) Prédiction du risque Cancer en fonction des antécédents familiaux (started in 2013)

In collaboration with D. Stoppa-Lyonnet (Institut Curie, Paris, France)

Funding (3 years): Ligue Nationale contre le Cancer (LNCC)

Eric Adjakossa (M2) Analyse longitudinale multivariée et application à des données immunologiques sur le paludisme (2013)

Funding (3 months): Internal

Tihninan Belaribi (M2) Prédiction du risque Cancer en fonction des antécédents familiaux (2013)

Funding (6 months): Internal

Rémi Bancal (M2) Plan d'expérience multifactoriel et adaptatif pour l'inférence de réseaux de gènes par Knock-out (2012)

In collaboration with A. Rau and F. Jaffrezic (INRA, Jouy-en-Josas, France)

Funding (6 months): Internal

Yousri Slaoui (M2) Caractérisation Génétique d'une Population Africaine, Imputation et Malaria (2012)

Funding (6 months): Internal

Aurélien Amchin (M2) Alternative Null Hypothesis for the Significance of Pairwise Alignment (2011)

Funding (5 months): Internal

Vittorio Perduca (Postdoc) Study of Arithmetic of Propagation in Bayesian Networks (2011-2012)

Funding (1 year): Fondation Sciences Mathématiques de Paris (FSMP)

The Minh Luong (Postdoc) Characterization of Tumoral DNA using SNPs Microarrays (2011-2013)

Funding (2 years): Université Paris Descartes

Djénéba Thiam (PhD) Longitudinal Data Analysis and Application to Immunology Data in a Malaria Study (2010-2014)

In collaboration with A. Garcia (IRD, Paris, France)

Funding (3 years): Université Paris Descartes

Vittorio Perduca (M2) Paternity Test and Bayesian Networks (2010)

Funding (3 months): Internal

Djénéba Thiam (M2) Censoring in Longitudinal Data Analysis and Application to Immunology Data in a Malaria Study (2010)

Funding (5 months): Internal

Imen Hammami (PhD) Statistical properties of Parasite Density Estimators in Malaria and Field Applications (2009-2013)

In collaboration with A. Garcia (IRD, Paris, France)

Funding (4 year): Université Paris Descartes

Stefan Wolfsheimer (Postdoc) Posterior Distribution of Score-Based Alignments (2009-2010)

Funding (2 years): Université Paris Descartes

Ahmed Fourati (M2) Exact propagation in Bayesia networks and application to the detection of genotyping error in pedigree analysis (2009)

Funding (2 months): Internal

Qiu Jiqiong (M1) Loi jointes et conditionnelles de nombres d'occurrences de motifs dans des chaînes de Markov (2009)

Funding (4 months): Internal

Marine Jeanmougin (M2) Evaluation et comparaison des tests statistiques d’expression différentiel entre deux conditions ; application au cancer du sein et à l’analyse simultanée génome/transcriptome (2009)

In collaboration with M. Guedj (LNCC, Paris, France)

Funding (5 months): LNCC

Phuong Le (M1) Alignement et chaînes de Markov cachées: estimation de fonctions de score (2008)

Funding (4 months): Internal

Houcine Ben Boussada (M2) libhmm: une librairie C++ pour l'estimation de chaînes de Markov cachées (2008)

Funding (2 months): Internal

Aline Gauliard (M1) Recherche de motifs structuraux fonctionnels dans les familles de SCOP (2008)

Funding (3 months): Internal

Yousri Slaoui (Postdoc) Longitudinal Analysis of Malaria Parasite Density Data (2007-2008)

Funding (1 year): Université Evry Val d'Essonne

Loïc Yengo (M1) Estimation of the scoring function in pairwise alignment (2007)

Funding (3 months): Internal

Allal Houssani (M2) Chaînes de Markov cachées pour l'estimation de fonction de score (2006)

Funding (5 months): Internal

Alexandre Jacob (L3) Statistiques de Motifs dans des Séquences Biologiques Segmentées (2005)

Adrien Gaillard (M2) Minimisation multidimensionnelle sous contraintes et application aux grandes déviations de niveau 2 pour les occurrences de mots dans des chaînes de Markov (2005)

Funding (3 months): Internal

Nathanaelle Brasseur (M2) Calcul de la fonction de répartition d'une forme quadratique en variables normales par inversion numérique de la fonction caractéristique (2005)

Funding (3 months): Internal

Mickaël Guedj (PhD) Méthodes Statistiques pour l'Analyse de Données Génétiques d'Association à Grande Echelle (2004-2007)

Funding (3 years): Serono Genetics Institute (CIFRE grant)

David Gomes (L3) Etude du spectre des matrices markoviennes estimées sur des séquences d'ADN (2004)

Maxime Huvet (M2) Détection ab initio de motifs biologiques dans les génomes (2004)

Funding (6 months): Internal

Mickaël Guedj (M2) Déséquilibre de liaison et association à la maladie dans les études de SNPs cas-témoins à grande échelle (2004)

Funding (6 months): Internal

Sabrina Serin (M1) Etude des origines biologiques de la présence de courts inverses-complémentaires dans les séquences d'ADN (2003)

Funding (6 months): Internal

Maxime Huvet (L3) Influence de l'évolution dans l'étude des motifs de fréquences exceptionnelles dans les séquences d'ADN (2002)

Adrien Richard (M1) WWbar: un outil pour étudier les courts inverse-complémentaires dans les séquences d'ADN (2002)

Funding (6 months): Internal


Publications

A new statistical method for curve group analysis of longitudinal gene expression data illustrated for breast cancer in the NOWAC postgenome cohort as a proof of principle (2016)

E. Lund, L. Holden, H. Bøvelstad, S. Plancade, N. Mode, C.-C. Günther, G. Nuel, J.-C. Thalabard, M. Holden

BMC Medical Research Methodology 16:28. DOI: 10.1186/s12874-016-0129-z

Identification of marginal causal relationships in gene networks, from observational and interventional expression data (2016)

G. Monneret, F. Jaffrezic, A. Rau and G. Nuel

Contributed talk, SMPGD, Lille, France.

An Adaptive Ridge Procedure for L0 Regularization (2016)

F. Frommlet and G. Nuel

PLoS ONE, DOI: 10.1371/journal.pone.0148620.

Counting Regular Expressions in Degenerated Sequences Through Lazy Markov Chain Embedding (2016)

G. Nuel and V. Delos

In International Conference on Computational Mathematics, Computational Geometry and Statistics (CMCGS). Proceedings, 124:235–46.

Detecting Gene-Environment Interaction using Breakpoint Models for Logistic Regression (2015)

F. Alarcon and G. Nuel

Invited talk, CMStatistics, London, UK.

Is It Possible to Detect GxE Interactions in GWAS When Causal Exposure Is Unobserved ? (2015)

F. Alarcon, V. Perduca and G. Nuel

Journal of Epidemiological Research 2(1): 109-117.

A Processual Model for Functional Analyses of Carcinogenesis in the Prospective Cohort Design (2015)

E. Lund, S. Plancade, G. Nuel, H. Bøvelstad and J.-C. Thalabard

Medical Hypotheses 85 (4). Elsevier: 494–97.

Prédiction de risque de cancer en fonction des antécédents familiaux (2015)

G. Nuel

Invited talk, 47ème journées de la SFdS, Lille, France.

Fast estimation of posterior probabilities in change-point models through a constrained hidden markov model (2015)

F. Alarcon and G. Nuel

Invited talk, The Danish Society for Theoretical Statistics Meeting, Copenhagen, Danemark

Estimation d’effets Causaux Dans Les Réseaux de Régulation Génique: Vers La Grande Dimension (2015)

G. Monneret, F. Jaffrézic, A. Rau and G. Nuel

Revue d’Intelligence Artificielle 29(2): 205–27.

Fast estimation of the Integrated Completed Likelihood criterion for change-point detection problems with applications to Next-Generation Sequencing data (2014)

A. Cleyen, T.M. Luong, G. Rigail and G. Nuel

Signal Processing, 98:233-242.

Estimation d’effets causaux dans les réseaux de régulation génique à partir d’observations (2014)

G. Monneret, F. Jaffrezic, A. Rau, and G. Nuel

Contributed talk, JFRB, IHP, Paris, France.

Estimating causal effects in gene expression from a mixture of observational and intervention experiments (2014)

A. Rau, F. Jaffrezic, and G. Nuel

Invited talk, Spring Meeting on Causality, IHP, Paris, France.

Estimating causal effects in gene expression from a mixture of observational and intervention experiments (2014)

G. Nuel, A. Rau, and F. Jaffrezic

Contributed talk, IBC, Florence, Italy.

GxE interactions detection in genome-wide association studies in presence of confounding factor (2014)

F. Alarcon, V. Perduca, and G. Nuel

Contributed talk, GDR Stat et Santé, Toulouse, France

Evidence for overdispersion in the distribution of malaria parasites and leukocytes in thick blood smears (2013)

I. Hammami, A. Garcia and G. Nuel

Malaria Journal, 12(1):398.

Fast estimation of posterior probabilities in change-point analysis through a constrained hidden Markov model (2013)

T. M. Luong, Y. Rozenholc and G. Nuel

Computational Statistics and Data Analysis, 68:129-140.

Joint estimation of causal effects from observational and intervention gene expression data (2013)

A. Rau, F. Jaffrézic and G. Nuel

BMC System Biology, 7(1):111.

Sparse approaches for the exact distribution of patterns in long state sequences generated by a Markov source (2013)

G. Nuel and J.-G. Dumas

Theoretical Computer Science, 479:22-42.

Measuring the influence of observations in HMMs through the Kullback-Leibler distance (2013)

V. Perduca and G. Nuel

IEEE Signal Processing Letters, 20(2):145-148.

Statistical Properties of Parasite Density Estimators in Malaria (2013)

I. Hammami, G. Nuel, A. Garcia

PLoS ONE, DOI: 10.1371/journal.pone.0051987.

Chapter: Hidden Markov Model Applications in Change-Point Analysis (2012)

T. M. Luong, V. Perduca, and G. Nuel

In "Hidden Markov Models - Applications in Signal, Image and Pattern Recognition", ISBN 980-953-307-564-3.

Computing Posterior Probabilities for Score-based Alignments Using ppALIGN (2012)

S. Wolfsheimer, A. K. Hartmann, R. Rabus, G. Nuel

Statistical Applications in Genetics and Molecular Biology, 11(4):1.

Alternative Methods for H1 Simulations in Genome Wide Association Studies (2012)

V. Perduca, C. Sinoquet, R. Mourad, G. Nuel

Human Heredity, 73(2):95-104.

ISL1 Directly Regulates FGF10 Transcription During HumanCardiac Outflow Formation (2012)

C. Golzio, E. Havis, P. Daubas, G. Nuel, C. Babarit, A. Munnich, M. Vekemans, S. Zaffran, S. Lyonnet and H. C. Etchevers

PLoS ONE, DOI: 10.1371/journal.pone.0030677.

Chapter: Significance Score of Motifs in Biological Sequences (2011)

G. Nuel

Bioinformatics - Trends and Methodologies, Mahmood A. Mahdavi (Ed.) ISBN: 978-953-307-282-1, InTech: 173-194.

Conotoxin protein classification using free scores of words and support vector machines (2011)

N. Zaki, S. Wolfsheimer, G. Nuel and S. Khuri

BMC Bioinformatics, 12:217.

On the first k moments of the random count of a pattern in a multi-states sequence generated by a Markov source (2010)

G. Nuel

Journal of Applied Probability, 47(4):1105-1123.

Should we abandon the t-test in the analysis of gene ex- pression microarray data: a comparison of variance modeling strategies (2010)

M. Jeanmougin, A. de Reynies, L. Marisa, C. Paccard, G. Nuel, and M. Guedj

PLoS ONE, DOI: 10.1371/journal.pone.0012336.

Deciphering normal blood gene expression variation - The NOWAC postgenome study (2010)

V. Dumeaux, K. S. Olsen, G. Nuel, R. H. Paulssen, A. L. Børresen-Dale, E. Lund

PLoS Genetics, 6(3).

Genome wide linkage study, using a 250K SNP map, of Plasmodium falciparum infection and mild malaria attack in a Senegalese population (2010)

J. Milet, G. Nuel, L. Watier, D. Courtin, Y. Slaoui, P. Senghor, F. Migot-Nabias, O. Gaye, A. Garcia.

PLoS ONE, DOI: 10.1371/journal.pone.0011616.

Exact distribution of a pattern in a set of random sequences generated by a Markov source: applications to biological data (2010)

G. Nuel, L. Regad, J. Martin, A.-C. Camproux

Algorithms for Molecular Biology, 5:15.

Mining protein loops using a structural alphabet and statistical exceptionality (2010)

L. Regad, J. Martin, G. Nuel, A.-C. Camproux

BMC Bioinformatics, 11:75.

Chapter: Finite Markov chain embedding for the exact distribution of patterns in a set of random sequences (2010)

J. Martin, L. Regad, A.-C. Camproux, and G. Nuel

In Advances in data analysis, Stat. Ind. Technol., p171–180.

Counting Patterns in Degenerated Sequences (2009)

G. Nuel

Lecture Notes in Computer Science: Pattern Recognition in Bioinformatics, 222-232.

kerfdr: A semi-parametric kernel-based approach to local False Discovery Rate estimations (2009)

M. Guedj, S. Robin, A. Celisse, G. Nuel

BMC Bioinformatics, 10:84.

Waiting Time Distribution for Pattern Occurrence in a Constrained Sequence: an Embedding Markov Chain Approach (2008)

G. Nuel

Discrete Mathematics and Theoretical Computer Science, 10(3):149-160.

Pattern Markov chains: optimal Markov chain embedding through deterministic finite automata (2008)

G. Nuel

Journal of Applied Probability 45(1):226-243.

Cumulative Distribution Function of a Geometric Poisson Distribution (2008)

G. Nuel

Journal of Statistical Computation and Simulation 78(3): 385-394.

A note on allelic tests in case-control association studies (2008)

M. Guedj, G. Nuel and B. Prum

Annals of Human Genetics, 72:407-409.

A PCSK9 variant and familial combined hyperlipidaemia (2008)

Abifadel M, Bernier L, Dubuc G, Nuel G, Rabès JP, Bonneau J, Marques A, Marduel M, Devillers M, Munnich A, Erlich D, Varret M, Roy M, Davignon J, Boileau C

Journal of Medical Genetics, 45(12):780-6.

Computing power in case-control association studies and application to meta-statistics (2007)

M. Guedj, E. Della-Chiesa, F. Picard and G. Nuel

Annals of Human Genetics, 71: 262-270.

PMC pour l'étude des occurrences de motifs dans les séquences markoviennes (2006)

G. Nuel

Habilitation à Diriger des Recherches (HDR) in mathematics, University of Evry

A fast, unbiased and exact allelic test for case-control association studies (2006)

M. Guedj, J. Wojcik, E. Della-Chiesa, G. Nuel and K. Forner

Human Heredity, 61(4):210-221.

Pattern statistics on Markov chains and sensitivity to parameter estimation (2006)

G. Nuel

Algorithms for Molecular Biology, 1(1):17.

Numerical solutions for Patterns Statistics on Markov chains (2006)

G. Nuel

Statistical Applications in Genetics and Molecular Biology, 5(1):26.

Detecting local high-scoring segments: a first stage approach for genome-wide studies (2006)

M. Guedj, D. Robelin, M. Hoebeke, M. Lamarine, J. Wojcik and G. Nuel

Statistical Applications in Genetics and Molecular Biology, 5(1):22.

Effective p-value computations using Finite Markov Chain Imbedding (FMCI): application to local score and to pattern statistics (2006)

G. Nuel

Algorithms for Molecular Biology, 1(1):5.

S-SPatt: Simple Statistics for Patterns on Markov chains (2005)

G. Nuel

Bioinformatics, 21(13):3051-3052.

LD-SPatt: Large Deviations Statistics for Patterns on Markov chains (2004)

G. Nuel

Journal of Computational Biology, 11(6):1023-1033.

AMIGene: Annotation of Microbial Genes (2003)

S. Bocs, S. Cruveiller, D. Vellenet, G. Nuel et C. Medigue

Nucleic Acid Research, 31(13):3723-3726.

SPA: Simple web tool to assess statistical significance of DNA patterns (2003)

H. Richard et G. Nuel

Nucleic Acid Research, 31(13):3679-3681.

Short inverse complementary amino acid sequences generate protein complexity (2003)

D. J. Goldstein, C. Fondrat, F. Muri, G. Nuel, P. Saragueta, A. S. Tocquet et B. Prum

CRAS Biologie, 154(1-2):170-180.

Grandes déviations et chaînes de Markov pour l'étude des occurrences de mots dans les séquences biologiques (2001)

G. Nuel

PhD thesis in mathematics, University of Evry.

Predicting distances using a linear model: the case of varietal distinctness (2001)

G. Nuel, C. Baril, S. Robin

Journal of Applied Statistics, 28(5):607-621.

Varietal distinctness assisted by molecular markers: a methodological approach (2001)

G. Nuel, C. Baril, S. Robin

Acta Horiculturae, Proceedings of the MMH congress, 546:65-71.


Software

waffect

A R package to simulate constrained phenotypes under a disease model H1. waffect (pronounced 'double-u affect' for 'weighted affectation') is a package to simulate phenotypic (case or control) datasets under a disease model H1 such that the total number of cases is constant across all the simulations. The package also makes it possible to generate phenotypes in the case of more than two classes, so that the number of phenotypes belonging to each class is constant across all the simulations. waffect is used to assess empirically the statistical power of Genome Wide Association studies.

CRAN page:

postCP

A R package to estimate posterior probabilities in change-point models using constrained HMM. The functions are used for change-point problems, after an initial set of change-points within the data has already been obtained. The function postCP obtains estimates of posterior probabilities of change-point and hidden states for each observation, and confidence intervals for the positions of the change-point. The function postCPsample obtains random samples of sets of change-points using the output of the postCP function.

CRAN page:

SPatt

SPatt (Statistic for Patterns) is a suite of C++ programs designed for the computation of pattern occurrences p-value on text. Assuming the text is generated according to Markov model, the p-value of a given observation is its probability to occur. The lower is the p-value, the more unlikely is the observation. For example, this tools can be used to find patterns with unusual behaviour in DNA or proteins sequences.

Version 1.x:

Version 2.x:

299834

km traveled for Science

1990

hours of academic teaching

624

article pages published

126390

Lines of R code written