3D Spatial Organization Within Tumors

Data Science Seminar September 29, 2020 1:00PM ET Martin Aryee, PhD Assistant Professor of Pathology, Harvard Medical School Assistant Molecular Pathologist, Massachusetts General Hospital Zoom link: https://dfci.zoom.us/j/95524743149?pwd=SzN4cjJZUnhsNVl3dXNmZjZ1N3F4QT09 Abstract: The spatial organization of biological systems can impart additional functionality beyond that of the individual components. This is true at a range of scales – from cells […]

Constructing Confidence Interval for RMST under Group Sequential Setting

Frontiers in Biostatistics Seminar October 13, 2020 1:00PM Lu Tian, PhD Associate Professor of Biomedical Data Science in the School of Medicine Stanford University It is appealing to compared survival distributions based on restricted mean survival time (RMST), since it generates a clinically interpretable summary of the treatment effect, which can be estimated nonparametrically without […]

Computational Biology of DNA Repair in Cancer

Data Science Seminar October 15, 2020 1:00PM ET Dominik Glodzik, PhD Repare Therapeutics Zoom link: https://bit.ly/DSOct15 Abstract: Whole genome sequences contain within them signatures of mutational processes. In particular, some of the mutation signatures relate to impaired DNA-repair in cancer cells. Accurate measurement of mutation signatures reveals the role of DNA-repair deficiencies in etiology and […]

DF/HCC Cancer Data Science Program &Harvard Chan Bioinformatics CoreJoint Symposium on scRNAseq Methodology

Monday, December 14, 3:00-4:30 PM ET RSVP https://bit.ly/CDSBioDec14 Speakers: Aedin Culhane, Senior Research Scientist, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health Isabella Grabski, PhD Student in Biostatistics, Harvard University Probabilistic gene barcodes identify cell-types in single-cell RNA-sequencing data Shannan Ho Sui, Senior Research Scientist, Harvard T.H. Chan School of Public Health […]

Frontiers in Biostatistics: Statistical Modeling and Adjustment for Sampling Biases

Frontiers in Biostatistics Seminar January 12, 2021 1:00PM Jing Ning, PhD Associate Professor. Department of Biostatistics Division of Quantitative Sciences The University of Texas M.D. Anderson Cancer Center Register at: https://bit.ly/FIBJan12 Abstract: Bias sampling mechanisms are commonly encountered in applications where the subjects in a target population are not given an equal chance to be selected, […]

Frontiers in Biostatistics: Group Sequential Design Assuming Delayed Benefit

February 9, 2021 1:00PM ET Keaven Anderson, PhD Scientific AVP, Methodology Research, Biostatistics at Merck Group Sequential Design Assuming Delayed Benefit Abstract: We consider an asymptotic approach to design of group sequential trials with a potentially delayed effects. Logrank, weighted logrank tests and combination tests are of primary interest, but we also consider restricted mean […]

Frontiers in Biostatistics: Distributed Statistical Learning and Inference in EHR and Other Healthcare Datasets

Frontiers in Biostatistics Seminar March 9, 2021 1:00PM Rui Duan, PhD Assistant Professor of Biostatistics Harvard TH Chan School of Public Health Distributed Statistical Learning and Inference in EHR and Other Healthcare Datasets Abstract: The growth of availability and variety of healthcare data sources has provided unique opportunities for data integration and evidence synthesis, which […]

Frontiers in Biostatistics: Single-Cell RNA-Seq Data Analysis Via a Regularized Zero-Inflated Mixture Model Framework

Frontiers in Biostatistics Seminar May 11, 2021 1:00PM Jianhua Hu, PhD Professor, Biostatistics (in Medicine and in the Herbert Irving Comprehensive Cancer Center) Director, Cancer Biostatistics Program Columbia University Register at: http://bit.ly/FIBMay21 Abstract: Applications of single-cell RNA sequencing in various biomedical research areas have been blooming. This new technology provides unprecedented opportunities to study disease […]

Frontiers in Biostatistics: Studies on COVID-19 and Cancer using National Real-World VA Data

Nathanael Fillmore is the Associate Director for Machine Learning and Advanced Analytics at the VA Boston Healthcare System’s Cooperative Studies Program Informatics Center. He leads a data science team focused on using machine learning and data science methods, in combination with the VA’s large clinical, genomic, and imaging databases, to generate knowledge and resources that […]