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Giovanni Parmigiani, Ph.D.

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Overview
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Models and software for predicting who is at risk of carrying genetic variants that confer susceptibility to cancer. Application to breast, ovarian, colorectal, pancreatic and skin cancer.

Statistical methods for the analysis of high throughput genomic data: analysis of cancer genome sequencing projects; integration of genomic information across technologies; cross-study validation of genomics results.

Statistical methods for comparative effectiveness research: comprehensive models for lifetime history of chronic disease outcomes; Bayesian meta-analysis; Bayesian causal inference; decision analysis.

Bayesian modeling and computation: multilevel models; decision theoretic approaches to inference; sequential experimental design and their application to adaptive and multistage studies in clinical and epidemiological research.
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Research
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The research activities and funding listed below are automatically derived from NIH ExPORTER and other sources, which might result in incorrect or missing items. Faculty can login to make corrections and additions.
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  1. R01CA262710 (PARMIGIANI, GIOVANNI LUIGI) Jul 1, 2021 - Jun 30, 2026
    NIH
    Statistical methods for cancer mutational signatures
    Role: Principal Investigator
  2. R21CA177233 (PARMIGIANI, GIOVANNI LUIGI) Mar 1, 2013 - Feb 29, 2016
    NIH
    Novel Tools for Familial Risk Prediction
    Role: Principal Investigator
  3. R01CA174206 (PARMIGIANI, GIOVANNI LUIGI) Feb 1, 2013 - Jan 31, 2019
    NIH
    Bioinformatics Tools for Genomic Analysis of Tumor and Stromal Pathways in Cancer
    Role: Principal Investigator
  4. T32GM074906 (IRIZARRY, RAFAEL ANGEL) Aug 1, 2006 - Sep 30, 2011
    NIH
    Predoctoral Biostatistics Training in Genetics/Genomics
    Role: Co-Principal Investigator
  5. R01CA105090 (PARMIGIANI, GIOVANNI) Sep 30, 2004 - Jun 30, 2010
    NIH
    Statistical Methods for Cancer Genes
    Role: Principal Investigator

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© 2024 President and Fellows of Harvard College
Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.