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DTSTART;TZID=America/New_York:20240405T130000
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DTSTAMP:20260429T114951
CREATED:20240205T132216Z
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UID:4900-1712322000-1712336400@ds.dfci.harvard.edu
SUMMARY:2024 Marvin Zelen Symposium: Health Disparities and Cancer
DESCRIPTION:Friday April 5\, 2024\n1:00-5:00PM ET \nYawkey Conference Center\n450 Brookline Ave\, Boston\, MA\nIn-person event only\, the recording will be available post-event. \nRegistration for in person attendance. \nInvited speakers: \n\nAdewole Adamson\, MD\, MPP\, Dell Medical School at the University of Texas at Austin\, “AI and Melanoma Disparities”\nCynthia Dwork\,PhD\, Harvard University\, “Individuals\, Groups\, Indistinguishability\, and Loss: 15 Years of Algorithmic Fairness in 20 Minutes”\nNate Fillmore\,PhD\, VA Boston Healthcare System and Harvard Medical School\, “Health Disparities and Cancer in the National VA Healthcare System”\nCarmen E. Guerra\, MD\, MSCE\, University of Pennsylvania\, “The (Mis)use of Race and Ethnicity in Biomedical Research”\nJinani Jayasekera\, PhD\, National Institute on Minority Health and Health Disparities\, “Personalized Clinical Decision Tools to Support Equitable Breast Cancer Care”\nBogdan Pasaniuc\,PhD\, University of California Los Angeles\, “Polygenic risk scoring in personalized medicine: promises and challenges”
URL:https://ds.dfci.harvard.edu/event/2024-marvin-zelen-symposium-health-disparities-and-cancer/
LOCATION:Dana-Farber Cancer Institute\, 450 Brookline Ave\, Boston\, MA\, 02215\, United States
CATEGORIES:Symposium
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DTSTART;TZID=America/New_York:20240411T160000
DTEND;TZID=America/New_York:20240411T170000
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UID:5053-1712851200-1712854800@ds.dfci.harvard.edu
SUMMARY:Data Integration in Statistical Inference and Risk Prediction
DESCRIPTION:Joint Seminar with Harvard TH Chan School of Public Health \nThursday April 11\, 2024 @ 4pm ET\nFXB Building Room 301 \nYu Shen PhD\nConversation with a Living Legend Professor & Chair ad interim\nDepartment of Biostatistics\nThe University of Texas MD Anderson Cancer Center \nIn comparative effectiveness research and risk prediction for rare types of cancer\, it is desirable to combine multiple sources of data\, e.g.\, the primary cohort data together with aggregate information derived from cancer registry databases. Such integration of data may improve statistical efficiency and accuracy of risk prediction\, but also pose statistical challenges for incomparability between different sources of data. We develop the adaptive estimation procedures\, which used the combined information to determine the degree of information borrowing from the aggregate data of the external resource. We apply the proposed methods to evaluate the long-term effect of several commonly used treatments for inflammatory breast cancer by tumor subtypes\, while combining the inflammatory breast cancer patient cohort at MD Anderson and external data.
URL:https://ds.dfci.harvard.edu/event/data-integration-in-statistical-inference-and-risk-prediction/
CATEGORIES:Seminar
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DTSTART;TZID=America/New_York:20240417T120000
DTEND;TZID=America/New_York:20240417T130000
DTSTAMP:20260429T114951
CREATED:20240415T133033Z
LAST-MODIFIED:20240423T144756Z
UID:5314-1713355200-1713358800@ds.dfci.harvard.edu
SUMMARY:Deconvolving Clonotype from High-Throughput Perturb-Seq and Novel Splice Identification of Prematurely Terminated mRNA
DESCRIPTION:Elevate @ Eleven CompBio Connections \nWednesday April 17th\, 2024\n12:00 – 1:00 PM\nZelen Commons\, Center for Life Sciences Building \nJared Brown\, PhD\, Research Fellow\, Data Science \n  \nLunch is provided.
URL:https://ds.dfci.harvard.edu/event/deconvolving-clonotype-from-high-throughput-perturb-seq-and-novel-splice-identification-of-prematurely-terminated-mrna/
CATEGORIES:Seminar
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