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Parmigiani Lab

Summary

The Parmigiani Lab is led by Giovanni Parmigiani, Ph.D., in the Department of Data Science at the Dana-Farber Cancer Institute and the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. The group focuses on Bayesian statistical methodology, prediction modeling, machine learning, and decision-theoretic approaches in cancer genetics and personalized medicine. They are widely recognized for developing genetic risk prediction models, including the BRCAPRO model for predicting breast and ovarian cancer susceptibility, and for co-developing longitudinal EHR frameworks such as ALADYNOULLI.

Research Focus

  • Bayesian Modeling in Oncology: Developing algorithms to analyze high-throughput genomic data, identify patient subtypes, and model disease risk over time.
  • Genetic Risk Assessment Tools: Creating and maintaining statistical models (such as BRCAPRO and the BayesMendel package) to estimate carrier probabilities for high-penetrance cancer susceptibility genes.
  • Data Integration and Meta-Analysis: Developing biostatistical methods for combining diverse datasets to improve clinical decision-making.

Citations