Hub members Have many expertise, covering most of the fields in bioinformatics and biostatistics. You'll find below a non-exhaustive list of these expertise

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Searched keyword : Statistical experiment design

Related people (4)

Marie-Agnès DILLIES

Group : HEAD - Hub Core

I obtained an engineering degree in Biomedical engineering from Université de Technologie de Compiègne (UTC) in 1989, a master degree in Control of Complex Systems from UTC in 1990, a PhD in Control of Complex Systems from UTC in 1993, a University Degree in Human Genetics from The University of Rennes 1 in 2001 and a master degree in Functional Genomics from University Paris Diderot (Paris 7) in 2002. I worked as a statistician at the Transcriptome and Epigenome Platform from 2002 to 2017, where I was responsible for the statistical analyses of the data and had an important training activity (on the campus and outside). Since 2015 I have been co-head of the Bioinformatics and Biostatistics Hub within the Center of Bioinformatics, Biostatistics and Integrative Biology (C3BI). I am co-director of the Pasteur course Introduction to Data Analysis and co-organiser of the sincellTE summer school (a school dedicated to single cell transcriptome and epigenome data analysis). I am also co-managing the StatOmique group which gathers more than 60 statisticians from France.

RNA-seqStatistical inferenceTranscriptomicsBiostatisticsApplication of mathematics in sciencesExploratory data analysisIllumina HiSeqStatistical experiment designSequencing

Projects (3)

Quentin GIAI

Group : - Hub Core



Projects (0)

    Emeline PERTHAME

    Group : Stats - Hub Core

    Since February 2017 Research engineer, Hub of Bioinformatics and Biostatistics of the C3BI, Institut Pasteur 2015-2017 Post doctoral position, team MISTIS, INRIA Grenoble Topic: Robust clustering and robust non linear regression in high dimension. Collaboration with Florence Forbes (INRIA). 2012-2015 PhD thesis in Statistics, Applied Mathematics Department of Agrocampus-Ouest, IRMAR UMR 6625 CNRS, Rennes Topic: Stability of variable selection in regression and classification issues for correlated data in high dimension. Supervisor: David Causeur (Agrocampus-Ouest, IRMAR). Education 2015 PhD thesis in Statistics, Applied Mathematics Department of Agrocampus-Ouest, IRMAR UMR 6625 CNRS, Rennes 2012 ISUP degree (Institut de Statistique de l’UPMC), Université Pierre et Marie Curie, Paris 2012 Master 2 of Statistics, Université Pierre et Marie Curie, Paris

    ClusteringModelingStatistical inferenceTranscriptomicsBiostatisticsExploratory data analysisDimensional reductionStatistical experiment designMultidimensional data analysis

    Projects (22)

    Stevenn VOLANT

    Group : Stats - Hub Core

    After a diploma of statistician engineer from the Ensai (Ecole Nationale de la Statistique et de l’Analyse de l’Information) and a Ph.D in applied mathematics in the Statistics & Genome lab (AgroParisTech), I worked as a developer for the XLSTAT software. I have implemented some statistical methods such as mixture models, log-linear regression, mood test, bayesian hierarchical modeling CBC/HB, … Then I worked as a head teacher in statistics for one year. I was recruited in the Bioinformatic and biostatistic hub of the C3BI (Center of Bioinformatics, Biostatistics and Integrative Biology) in 2014, I am in charge of the statistical analysis and the development of R/R shiny pipelines.

    Machine learningStatistical inferenceTargeted metagenomicsBiostatisticsApplication of mathematics in sciencesStatistical experiment design

    Projects (34)

    Related projects (4)


    Complex chronic diseases such as type 1 and 2 diabetes are caused by the accumulation of genetic, microbial and lifestyle factors. The number and complexity of such factors makes prediction of pathogenesis and therapy particularly difficult. Although a single factor is rarely sufficient to trigger pathology, genetic and environmental factors have so far been studied in isolation. Nevertheless, a substantial number of genetic variants have been associated with disease risk and the concomitant lifestyle shift and excessive hygiene are thought to contribute to the increased incidence in inflammatory diseases in industrialized countries. Moreover, clinical and experimental observations suggest a strong impact of gut microbiota on susceptibility to inflammatory diseases. The aim of 3DPATH is to explore the multiplicity and complexity of genetic, microbial and lifestyle factors associated with vulnerability to inflammatory pathology, with a particular focus on type 1 and 2 diabetes, using mice of the Collaborative Cross (CC), that model human genetic variability. Quantitative trait loci analyses, as well as integrative data analyses on metabolic and inflammatory outcomes will reveal new genetic variants and combinations of variants associated with disease susceptibility, and whether alterations of the gut microbiota in genetically susceptible mouse strains can trigger the phenotype. Moreover, the longitudinal design of experiments will allow us to identify early biomarkers that predict the pathology later in life. In a second step, validation of the identified risk factor combinations and exploration of the underlying molecular mechanisms will be performed taking advantage of the mouse model.

    Project status : In Progress