Jean-Eudes Dazard, PhD
Assistant Professorjeanfirstname.lastname@example.org 216-368-3157 (o)
I've initially been trained in France in Computer Science then in Molecular Biology and Bioinformatics and lately in the US in Statistics. This mixed background allowed me to engage in research and teaching activities at the interface of these three areas. Over the last 15 years, and especially since my starting date as a faculty at CWRU, I've been the witness of tremendous changes in these disciplines not only on their own but also how they intersect and complement each other. Now more than ever is an exciting time to work in this evolving field of Data Science to constantly create new and powerful ways to study and model complex systems and phenomenon such as in genomics, proteomics, metabolomics and biomedical science. My research interest is focused on real-world research problem in computational and statistical biology with emphasis on developing data mining methods in high-dimensional data, mostly from high-throughput technologies. Because of my early background in computer science, I've always devoted time to develop computational resources such as webtools and softwares. Recently, I have been successful in securing NIH funding to support a long-term research project in so-called “Survival Bump Hunting for the Identification and Characterization of Informative Subgroups of Patients in High Dimensional Data" with direct clinical applications in diagnostic and prognostic tools for precision medicine. This has lead so far to the publication of a few articles, book chapter, websites and softwares (see references in sections below and following links).
2013-2017 NIH NCI Research Grant Principal Investigator (R01 CA 160593)
2003-2006 NIH NCI CoGEC Training Grant Fellow (R25 CA 094186)
2001-2003 Sir Charles Clore Fellow, Weizmann Institute of Science, Rehovot, Israel
2000-2001 French Society of Dermatology (SFD) and L’Oreal co. Award (€45,000), Paris, France
2006 - Present: Biostatistics Consultant in the Biostatistics and Bioinformatics Core Facility (BBCF) of the Comprehensive Cancer Center (CCC). http://cancer.case.edu/research/sharedresources/biostatistics/
2009 - Present: Assistant Professor in Center for Proteomics and Bioinformatics (CPB) of the School of Medicine. http://proteomics.case.edu/faculty/jean-eudes_dazard.html
2015 - Present: Assistant Professor in Dept of Department of Nutrition, Metabolic and Protein Science of the School of Medicine. http://case.edu/medicine/nutrition/
Conventional statistical models are inappropriate when dealing with large datasets where the number of variables exceeds the number of observations (so-called p >> n paradigm). It is a challenging problem causing severe risks of model unfitting and statistical errors. Particular issues posed by high dimensional data are the control of error rates due to inherent noise of the employed technologies, the multi-collinearity of predictors due to the parallel nature variables interrogation, and the sparsity of informative predictors due to the massive number of variables interrogated compared to the fewness of variables at play.
My research interest is in computational/statistical biology with emphasis on developing data mining methods in high dimensional data, or "omics" data, mostly arising from high-throughput technologies such as microarrays, proteomics and high-throughput sequencing technologies. My focus has recently been in:
- Bump hunting in Classification, Regression, and Survival settings. General applications are in developing risk and reliability analysis tools in an increasing range of sciences. One of which currently in development is in “Survival Bump Hunting for Identifying and Characterizing Informative Subgroups of Patients in High Dimensional Data" with direct clinical applications in diagnostic and prognostic tools for precision medicine.
- Model Selection and Predictive Modeling as applied to Differential Expression and Genetic Interaction problems. Recent discoveries were made in genetic association studies, biomarker discovery, and proteomics interaction problems.
- Regularization and Variance Stabilization of high-dimensional data.
- Statistical Computing: Resampling and Monte-Carlo methods. Parallel Computing and Computational Complexity. Source Code Management and Collaborative Software Development (GitHub).
Publications In Preparation or Submitted
- DAZARD J-E., CHOE M., RAO J.S. PRIMsrc for Identification and Characterization of Informative Prognostic Subgroups by Survival Bump Hunting. (in prep 2016).
- DIAZ D.A., RAO J.S., DAZARD J-E. On the Explanatory Power of Principal Components. (in prep 2016). Archives of Cornell University Library: http://arxiv.org/abs/1404.4917
- DAZARD J-E., ISHWARAN H., MEHLOTRA R.K., MARTINSON J.J., PENUGONDA S., HUSSAIN S., BREAM J., DUGGAL P., JUREVIC R.J., CHANCE M., WEINBERG A., ZIMMERMAN P.A. Ensemble Survival Tree Models Reveal Genetic Interactions and their Association with Clinical Time-to-Events. (in prep 2016).
- DAZARD J-E., RAO J.S. Variable Selection Strategies for High-Dimensional Survival Bump Hunting using Recursive Peeling Methods. (in prep 2016).
- DAZARD J-E., PAWITAN Y., RAO J.S. Identification and Genomic Characterization of Informative Prognostic Subgroups in Lung Cancer Patients by Sparse Survival Bump Hunting. (in prep 2016).
- STETSON D.M., DAZARD J-E., BARNHOLTZ-SLOAN J. Protein Markers Predict Survival in Glioma Patients. Mol. Cell. Proteomics (in press 2016). PMCID: pending.
- DAZARD J-E., CHOE M., LEBLANC M., RAO J.S. Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods. Statistical Analysis and Data Mining (2016) 9(1):12-42. PMCID: PMC4809437.
- DAZARD J-E., CHOE M., LEBLANC M., RAO J.S. R package PRIMsrc: Bump Hunting by Patient Rule Induction Method for Survival, Regression and Classification. In JSM Proceedings, Statistical Programmers and Analysts Section. Seattle, WA, USA. American Statistical Association-IMS (2015) p. 650-664. PMCID: PMC4718587.
- DAZARD J-E., CHOE M., LEBLANC M., RAO J.S. Cross-Validation of Survival Bump Hunting using Recursive Peeling Methods. In JSM Proceedings, Survival Methods for Risk Estimation/Prediction Section. Boston, MA, USA: American Statistical Association-IMS (2014) p. 3366-3380. PMCID: PMC4795911.
- DAZARD J-E., SANDLERS Y., DOERNER S, BERGER N.A., BRUNENGRABER H. Metabolomics in APCMin/+ Mice Genetically Susceptible to Intestinal Cancer. BMC System Biology (2014) 8:72-93. PMCID: PMC4099115.
- DIAZ D.A., RAO J.S., DAZARD J-E. Optimization of the Patient Rule Induction Method (PRIM) under Normality. Complex Data Modeling and Computationally Intensive Statistical Methods for Estimation and Prediction (S.Co. 2013). Milan, Italy. PMCID: NA.
- SAHA S., DAZARD J-E., XU H., EWING R.M. Computational Framework for Analysis of Prey-Prey Associations in Interaction Proteomics Identifies Novel Human Protein-Protein Interactions and Networks. J. Proteome Research (2012) 11(9):4476-87. PMCID: PMC3610425.
- DAZARD J-E., SAHA S., EWING R.M. ROCS: A Reproducibility Index and Confidence Score for Interaction Proteomics Studies. BMC Bioinformatics (2012) 13(1) 128. PMCID: PMC3568013.
- DAZARD J-E., RAO J.S. MARKOWITZ S. Local Sparse Bump Hunting Reveals Molecular Heterogeneity of Colon Tumors. Statistics in Medicine (2012) 31(11-12), 1203-1220. PMCID: PMC3668571.
- DAZARD J-E., RAO J.S. Joint Adaptive Mean-Variance Regularization and Variance Stabilization of High Dimensional Data. Comput. Statist. Data Anal. (2012) 56(7): 2317-2333. PMCID: PMC3375876.
- SCHLATZER D.M.*, DAZARD J-E.* (*equal), EWING R.M., ILCHENKO S., TOMECHEKO S.E., EID S., HO V., YANIK G., CHANCE M.R., COOKE K.R. Human Biomarker Discovery and Predictive Models for Disease Progression in Idiopathic Pneumonia Syndrome Following Allogeneic Stem Cell Transplantation. Mol. Cell. Proteomics (2012) 11(6): 1-15. PMCID: PMC3433920.
- DAZARD J-E., XU H., RAO J.S. R package MVR for Joint Adaptive Mean-Variance Regularization and Variance Stabilization. In JSM Proceedings, Statistical Programmers and Analysts Section. Miami Beach, FL, USA. American Statistical Association-IMS (2011) p. 3849-3863. PMCID: PMC4725579.
- DAZARD J-E., RAO J.S. Regularized Variance Estimation and Variance Stabilization of High-Dimensional Data. In JSM Proceedings, High-Dimensional Data Analysis and Variable Selection Section. Vancouver, BC, Canada. American Statistical Association-IMS (2010) p. 5295-5309. PMCID: PMC4727967.
- DAZARD J-E., RAO J.S. Local Sparse Bump Hunting. J. Comp Graph. Statistics (2010) 19(4): 900-929. PMCID: PMC3293195.
- DIAZ D.A., DAZARD J-E., RAO J.S. Unsupervised Bump Hunting Using Principal Components. In: Ahmed SE, editor. Big and Complex Data Analysis: Methodologies and Applications. Contributions to Statistics, vol. Edited Refereed Volume. Cham Heidelberg New York: Springer (in press 2016). PMCID: pending. Archives of Cornell University Library: http://arxiv.org/abs/1409.8630.
- DAZARD J-E., CHOE M., LEBLANC M, SANTANA A. Contributed R package PRIMsrc: Bump Hunting by Patient Rule Induction Method for Survival, Regression and Classification. GitHub repository (2015): https://github.com/jedazard/PRIMsrc. Comprehensive R Archive Network (CRAN) (2015): https://cran.r-project.org/web/packages/PRIMsrc/index.html
- DAZARD J-E., XU H., SANTANA A. Contributed R package MVR: Mean Variance Regularization. GitHub repository (2015): https://github.com/jedazard/MVR. Comprehensive R Archive Network (CRAN) (2011): https://cran.r-project.org/web/packages/MVR/index.html
- DAZARD J-E., SANTANA A. Contributed R package ROCS: Reproducibility Index and Confidence Score for Interaction Proteomics Studies. GitHub repository (in prep 2016): https://github.com/jedazard/ROCS
- DAZARD J-E., SANTANA A. Contributed R package LSBH: Local Sparse Bump Hunting. GitHub repository (in prep 2016): https://github.com/jedazard/LSBH.