R and Bioconductor in Cancer Research – Big Data in Genomics
R and Bioconductor in Cancer Research – Big Data in Genomics


In 2015 President Obama launched the Precision Medicine Initiative, a research effort to revolutionize how we treat disease. It aims to tailor treatment to an individual patient's unique characteristics including their genome and health history. Its success in cancer research relies on the development and interpretation of statistical models that integrate disparate data sources of genomic, clinical and patient data. I will present how we use R, Bioconductor and cloud infrastructure to integrate and analyze multiple data of genomics data from over 10,000 cancer patients.


Dr. Culhane is a research scientist in Biostatistics and Computational Biology at Dana Farber Cancer Institute and at Harvard TH Chan School of Public Health where she develops and applies multivariate statistics and machine learning to cancer genomics and genetics data. She has a PhD from the University of Manchester, UK and has published over 50 peer reviewed research articles. She is an R/Bioconductor developer, has written Bioconductor packages for biclustering and exploratory data analysis of big data in genomics and isa member of the technical advisory board to Bioconductor. Bioconductor is an a open-source, open development software project, primarily in R with over 1,200 packages, for the analysis of genomics data. She is a founding member of the Boston R/Bioconductor for Genomics Meetup. She taught Bio503, Introduction to programming and statistical modeling in R at the Harvard TH Chan School of Public Health (2008-2016).

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