Annette Molinaro, Ph.D.
Assistant
Professor,
Biostatistics
Dr. Molinaro's research interests are primarily focused on statistical genetics and computational biology, including piecewise constant estimation for prediction, survival analysis, classification, and causal inference with additional curiosities in cancer epidemiology as in the estimation of absolute risk in nested case-control studies.
Her research has pertained to predicting censored and non-censored outcomes with high-dimensional explanatory variables, such as microarrays, and large scale epidemiology studies. This has included an adaptation to Classification and Regression Trees (CART) for survival outcomes, the introduction of a novel data-adaptive algorithm that builds Boolean combinations of explanatory variables, and a non-parametric method for point estimation based on a nested case-control study design. In addition, she has worked with collaborators at the National Cancer Institute (NCI) on comparing cross-validation approaches to validating predictors in small sample sizes. Dr. Molinaro's current focus is on exploring new algorithms for building predictors with high-dimensional data structures including genomic and proteomic data.
Education
Ph.D., University of California, Berkeley, 2004
M.A. in Biostatistics, University of California, Berkeley, 2000
Fellowship, Cancer Prevention Fellowship Program, National Cancer Institute
Awards and Honors
Selected as Yale School of Public Health’s Teacher of the Year, 2008.
Elected Member of the International Statistical Institute, 2006.
Junior Investigator’s Workshop, 2006 Eastern North American Region Annual Meeting honorarium
National Science Foundation and Association for Women in Mathematics Travel Grant, International Workshop on Statistical Modelling, 2005
New Investigator’s Workshop, 2005 American Society of Preventive Oncology Annual Meeting honorarium
Evelyn Fix Prize, University of California, Berkeley, May 2004
Chin Long Chiang Biostatistics Student of the Year, University of California, Berkeley, May 2004
Professional Services
Cooperative International Neuromuscular Research Group (CINRG) Therapeutic Subcommittee
Faculty Mentor, Mauro School in New Haven, Connecticut
Courses Taught
BIS 525A Biostatistics Seminar Series, Fall 2005, Spring 2006, Fall 2006
BIS 635B Topics in Statistical Epidemiology, Spring 2006
BIS 505B Introduction to Statistical Thinking II, Spring 2007
Biostatistics Primer Yale University School of Medicine Continuing Education, Spring 2007
BIS 632B Design of Epidemiology Studies, Spring 2008
BIS 630B Applied Survival Analysis, Spring 2008
Biostatistics Intensive Advanced Professional M.P.H. Program, Yale University School of Public Health, Summer 2008 and 2009
Yale Affiliations
Co-Director, Biostatistics and Bioinformatics Core for the Skin Cancer (SPORE), Yale Cancer Center
Assistant Professor, Computational Biology and Bioinformatics in Biological and Biomedical Sciences.
Training Faculty Member, Yale University-NCI Pre-doctoral Training Program in Cancer Epidemiology
In the News
YSPH Biostatistics Faculty Lead Cancer Risk Workshop in Banff
YSPH 2008 Teacher of the Year
Molinaro Co-Author on Study
Awarded $11.5 Million to Research and Treat Skin Cancer
Molinaro Elected Member of International Statistical Institute
Molinaro Receives Transition Career Development Award to Study Cancer Prediction
Selected Publications
Koga, Y., Pelizzola, M., Cheng, E., Krauthammer, M., Molinaro, A.M., Halaban R., and Weissman S. Genome-Wide Screen of Promoter Methylation Identifies Novel Markers for Tumor Development in Melanoma. Genome Research. In Press.
Molinaro, A.M. and Lostritto, K. Statistical resampling for large screening data analysis such as classical resampling, Bootstrapping, Markov chain Monte Carlo, and statistical simulation and validation strategies. STATISTICAL BIOINFORMATICS: A GUIDE FOR LIFE AND BIOMEDICAL SCIENCE RESEARCHERS. Ed. Jae K. Lee. John Wiley & Sons, Inc. In press, Invited Chapter to be published in 2009.
Pelizzola, M., Koga, Y., Urban, A.E., Krauthammer, M.,Weissman, S., Halaban, R., and Molinaro, A.M. MEDME: An experimental and analytical methodology for the estimation of DNA methylation levels based on microarray derived MeDIP-enrichment. Genome Research 18:1652–1659, 2008.
Petersen, M.L., Molinaro, A.M., Sinisi, S.E. and van der Laan, M.J. Cross-Validated Bagged Learning. Journal of Multivariate Analysis 98(9):1693–1704, 2007.
Hothorn, T., Buhlmann, P., Dudoit, S., Molinaro, A.M., and van der Laan, M.J. Survival Ensembles. Biostatistics 7: 355-373, 2006.
Molinaro, A.M., Simon, R., and Pfeiffer, R.M. Prediction Error Estimation: A Comparison of Resampling Methods. Bioinformatics 21(15): 3301-3307, 2005.
Molinaro, A.M. and van der Laan, M.J. An Application of Cross-validating and Bagging Partitioning Algorithms with Variable Importance. Proceedings of the 20th International Workshop on Statistical Modelling 2005.
Molinaro, A.M., Dudoit, S., and van der Laan, M.J. Tree-based Multivariate Regression and Density Estimation With Right-censored Data. Journal of Multivariate Analysis 90(1): 154-177, 2004.
Molinaro, A.M. and van der Laan, M.J. A New Partitioning Algorithm for Prediction of Survival Outcomes: Illustration with Histogram Regression. Proceedings of the American Statistical Association 2004.
Kerlikowske, K., Molinaro, A.M., Cha, I., Ljung, B.M., Ernster, V., Stewart, K., Chew, K., Moore, D., and Waldman, F. Predictors of Recurrence among Women with DCIS Treated by Lumpectomy. Journal of National Cancer Institute 95(22): 1692-1702, 2003.
Dudoit, S., van der Laan, M.J., Keles, S., Molinaro, A.M., Sinisi, S., and Teng, S.L. Loss-based Estimation with Cross-validation: Applications to Microarray Data Analysis and Motif Finding. SIGKDD Explorations 5(2): 37-49, 2003.
Aust, D.E., Terdiman, J.P., Willenbucher, R.F., Chang, C.G., Molinaro, A.M., Baretton, G.B., Loehrs, U., and Waldman, F.M. The Apc/beta-catenin Pathway in Ulcerative Colitis-related Colorectal Carcinomas: A Mutational Analysis. Cancer 94(5): 1421-1427, 2002.
For a further list of Dr. Molinaro’s publications, please see PubMed.
Software
Open Source Packages
Pelizzola, M. and Molinaro, A.M. MEDME: Modeling Experimental Data from MeDIP Enrichment. http://www.bioconductor.org/packages/2.3/bioc/html/MEDME.html
Molinaro, A.M., Lostritto, K., and Weston, S. partDSA: Partitioning using deletion, substitution, and addition moves.
http://cran.r-project.org/web/packages/partDSA/index.html |
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