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DTSTART;TZID=America/New_York:20211019T130000
DTEND;TZID=America/New_York:20211019T140000
DTSTAMP:20260407T224715
CREATED:20210928T181448Z
LAST-MODIFIED:20211201T165250Z
UID:3058-1634648400-1634652000@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Treatment-free Survival as a Novel Outcome Measure of Immuno-oncology-based Therapy
DESCRIPTION:Tuesday\, October 19\, 2021\n1:00pm Eastern Time \nMeredith Regan\, ScD\nAssociate Professor\nDepartment of Data Science\, Dana-Farber Cancer Institute\,\nHarvard Medical School \nTreatment-free Survival as a Novel Outcome Measure of Immuno-oncology-based Therapy \nYouTube Link
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-meredith-regan/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2021/09/preferred_meredith_regan_square.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220111T130000
DTEND;TZID=America/New_York:20220111T140000
DTSTAMP:20260407T224715
CREATED:20211130T233743Z
LAST-MODIFIED:20220111T182225Z
UID:3204-1641906000-1641909600@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Studies on COVID-19 and Cancer using National Real-World VA Data
DESCRIPTION: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 will improve care for Veterans with cancer. He also holds a faculty position at Harvard Medical School and is site director for the VA/NCI Big Data-Scientist Training Enhancement Program. Previously\, he was a post-doctoral fellow at Dana-Farber Cancer Institute and Harvard School of Public Health under the direction of Giovanni Parmigiani and Nikhil Munshi. \nThis seminar was not recorded. 
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-studies-on-covid-19-and-cancer-using-national-real-world-va-data/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/11/headshot-square-small.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220204T130000
DTEND;TZID=America/New_York:20220204T140000
DTSTAMP:20260407T224715
CREATED:20220202T172751Z
LAST-MODIFIED:20220202T172751Z
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SUMMARY:Data Science Seminar: Deciphering Tissue Microenvironment from Next Generation Sequencing Data
DESCRIPTION:Friday February 4\, 2022\n1:00PM Eastern Time \nRegister. \nJian Hu\nPhD Candidate\, Department of Biostatistics\, Epidemiology and Informatics\nUniversity of Pennsylvania \nABSTRACT: The advent of high-throughput next-generation sequencing (NGS) technologies has transformed our understanding of cell biology and human disease. As NGS has been adopted earliest by the scientific community\, its use has now become widespread\, and the technology has improved rapidly. At present\, it is now common for laboratories to assay genome-wide transcriptomes of thousands of cells in a single scRNA-seq experiment. In addition\, technologies that enable the measurement of new information\, for example\, chromatin accessibility\, protein quantification\, and spatial location\, have been developed. In order to take full advantage of the multi-modality information when analyzing NGS data\, new methods are demanded. This seminar will introduce several machine learning algorithms for NGS data analysis with different aims\, including cell type classification\, spatial domain detection\, and tumor microenvironment annotation. \nParts of the talk are based on the following two papers: \n\nhttps://www.nature.com/articles/s42256-020-00233-7 \n\nhttps://www.nature.com/articles/s41592-021-01255-8 \nKEYWORDS: single cell RNA sequencing (scRNA-seq)\, Spatial transcriptomics (ST)\, tumor microenvironment\, machine learning
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-deciphering-tissue-microenvironment-from-next-generation-sequencing-data/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/02/WechatIMG25-1.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220208T130000
DTEND;TZID=America/New_York:20220208T140000
DTSTAMP:20260407T224715
CREATED:20210614T165138Z
LAST-MODIFIED:20220215T201001Z
UID:2802-1644325200-1644328800@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Early Phase Design Considerations for Oncology Drug Development in the Era of Immunotherapy and Targeted Agents
DESCRIPTION:Tuesday\, February 8\, 2022\n1:00pm Eastern Time \nYouTube Video \nElizabeth Garrett-Mayer\, PhD\, FSCT\nVice President\nCenter for Research and Analytics (CENTRA) \nDr. Garrett-Mayer joined ASCO in 2017 as CENTRA’s Division Director for Biostatistics and Research Data Governance and became CENTRA’s first Vice President in 2022. CENTRA leads ASCO’s research efforts\, including the TAPUR Study\, ASCO’s COVID-19 Registry\, and research projects aimed at increasing minority enrollment and expanding eligibility criteria in clinical trials. Prior to joining ASCO\, she served on the faculty in the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins in the Department of Oncology\, and then joined the faculty of the Medical University of South Carolina (MUSC) and established the Biostatistics Shared Resource at the Hollings Cancer Center (HCC). \nShe earned her bachelor’s degree from Bowdoin College in Math and Economics\, and a PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health. Her publication record includes more than 280 peer-reviewed publications\, primarily in cancer research and research methods. She has also been a member of numerous NIH grant review committees\, Data Safety Monitoring Boards for NIH-supported clinical trials and has served on the editorial board of three peer-reviewed journals (Clinical Trials\, Cancer\, and the Journal of the National Cancer Institute)\, and as faculty on the ASCO/AACR Methods in Clinical Cancer Research Workshop for over a decade. \n  \nAbstract: \nUncertainty about optimality of doses of anti-cancer agents approved in recent years\, including PD-L1 blockade agents and targeted agents\, has led to a movement in the oncology drug development field to reconsider the traditional approach for drug development.  Following in the path of approvals for nivolumab and pembrolizumab\, trial designs for dose finding trials have ballooned from sample sizes in the range of 12-50 to the hundreds or more.  In addition\, post-marketing trials have demonstrated that lower doses of certain drugs may be as efficacious as approved doses.  The has contributed to a renewed focus on ‘dose optimization\,’ causing statisticians\, researchers\, patient advocates and regulators to realize the profound inadequacies of traditional phase I dose finding designs. Trial approaches need to incorporate the adaptive nature of a seamless toxicity-efficacy paradigm\, while maintaining practical aspects of trial implementation\, statistical properties\, endpoint measurement and safety and well-being of patients. This talk will discuss these aspects and provide recent context of drug approvals in oncology.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-elizabeth-garrett-meyer/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/06/headshot_sq.png
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220215T130000
DTEND;TZID=America/New_York:20220215T140000
DTSTAMP:20260407T224715
CREATED:20220208T194759Z
LAST-MODIFIED:20220215T200828Z
UID:3322-1644930000-1644933600@ds.dfci.harvard.edu
SUMMARY:Data Science Seminar: End-to-end AI for Screening Mammography
DESCRIPTION:Tuesday February 15\, 2022\n1:00PM Eastern Time \nWilliam Lotter\, PhD\nVice President of Machine Learning\, RadNet\, Inc.\nChief Technology Officer & Co-Founder\, DeepHealth\, Inc. \nRegister. \nScreening mammography has been estimated to reduce breast cancer mortality by 20-40%\, but significant opportunities remain for improving access and overall quality. Artificial intelligence (AI) has the potential to deliver these improvements\, but developing clinically-effective AI presents additional challenges spanning development through clinical integration. In this talk\, I will present an AI algorithm that addresses the “needle-in-a-haystack” nature of mammography while efficiently using labeled training data and enabling localization-based explainability. I will then detail the algorithm’s performance\, providing evidence of generalization across populations and an ability to aid in earlier cancer detection. Finally\, I will discuss our efforts in achieving regulatory clearance and large-scale clinical deployment.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-end-to-end-ai-for-screening-mammography/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2022/02/bill-lotter.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220224T130000
DTEND;TZID=America/New_York:20220224T140000
DTSTAMP:20260407T224715
CREATED:20220218T134643Z
LAST-MODIFIED:20220218T175729Z
UID:3355-1645707600-1645711200@ds.dfci.harvard.edu
SUMMARY:Data Science Seminar: Spatial meshing for general Bayesian multivariate models
DESCRIPTION:Thursday February 24\, 2022\n1:00PM Eastern Time \nMichele Peruzzi\, PhD\nPostdoctoral Associate\, Department of Statistical Science\, Duke University \nRegister. \nAbstract: In this talk\, I will consider the problem of fitting Bayesian models with spatial random effects to large scale multivariate multi-type data from satellite imaging\, land-based weather and air quality sensors\, and citizen science\, with diverse applications in the environmental sciences\, ecology\, and public health. \nIn these contexts\, the goal of quantifying spatial associations via random effects in Bayesian hierarchical models can be achieved by letting a Gaussian process (GP) characterize dependence in space\, time\, and across outcomes. GPs have desirable properties and lead to extremely flexible models\, which are able to accurately quantify uncertainties. Unfortunately\, GPs are notoriously poor performers in large data settings. While the literature on scalable GPs has primarily focused on their well-known bottlenecks in models of univariate continuous outcomes\, I consider the more challenging hurdles to efficient computations facing latent models of multivariate non-Gaussian outcomes.\nI introduce spatial meshing and manifold preconditioning as tools for efficient computations of multivariate Bayesian models of spatially referenced non-Gaussian data. First\, I outline spatial meshing as a tool for building scalable processes using patterned directed acyclic graphs on partitioned spatial domains. Then\, I present manifold preconditioning as a novel Langevin method for superior sampling performance with non-Gaussian multivariate data that are common in studying species’ communities. \nIn addition to these main topics\, I discuss additional strategies for improving Markov-chain Monte Carlo performance\, concluding with applications showcasing the flexibility of the proposed methodologies. All presented methods are implemented in the high performance R package ‘meshed’\, available on CRAN.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-spatial-meshing-for-general-bayesian-multivariate-models/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/02/thumb_image_1811412.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220228T130000
DTEND;TZID=America/New_York:20220228T140000
DTSTAMP:20260407T224715
CREATED:20220217T180736Z
LAST-MODIFIED:20220218T174651Z
UID:3347-1646053200-1646056800@ds.dfci.harvard.edu
SUMMARY:Data Science Seminar: From descriptive to predictive biology via single-cell multiomics
DESCRIPTION:Monday February 28\, 2022\n1:00PM Eastern Time \nGenevieve Stein-O’Brien\nInstructor\, Johns Hopkins University\, School of Medicine\nDepartment of Oncology\, Division of Biostatistics and Bioinformatics;\nDepartment of Neuroscience; and McKusick-Nathans Department of Genomic Medicine\nAssistant Director\, Johns Hopkins University Single Cell Consortium \nRegister. \nAbstract: As the single-cell field races to characterize each cell type\, state\, and behavior\, the complexity of the computational analysis approaches the complexity of the biological systems. Single cell and imaging technologies now enable unprecedented measurements of state transitions in biological systems\, providing high-throughput data that capture tens-of-thousands of measurements on hundreds-of-thousands of samples. Thus\, the definition of cell type and state is evolving to encompass the broad range of biological questions now attainable. To answer these questions requires the development of computational tools for integrated multiomics analysis. Merged with statistical and mathematical models\, these algorithms will be able to forecast future states of biological systems\, going from statistical inferences of phenotypes to time course predictions of the biological systems with dynamic maps. Thus\, systems biology for forecasting biological system dynamics from multiomic data represents the future of cell biology empowering a new generation of technology-driven predictive medicine.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-from-descriptive-to-predictive-biology-via-single-cell-multiomics/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/02/1609946354584.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220301T130000
DTEND;TZID=America/New_York:20220301T140000
DTSTAMP:20260407T224715
CREATED:20220105T134746Z
LAST-MODIFIED:20220304T192258Z
UID:3245-1646139600-1646143200@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Considerations for Extracting Real-World Evidence from Real-World Data
DESCRIPTION:Tuesday\, March 1\, 2022\n1:00pm Eastern Time \nRebecca A. Hubbard\, PhD\nProfessor of Biostatistics\nUniversity of Pennsylvania Perlman School of Medicine \nYouTube Video \nAbstract: Opportunities to use real-world data (RWD)\, including electronic health records (EHR) and medical claims data\, have exploded over the past decade. The Covid-19 pandemic has provided a particularly dramatic illustration of the potential value of RWD for advancing medical research as well as the high-risk of bias inherent to many such studies. Using data sources that were not collected for research purposes comes at a cost\, and naïve use of these data without considering their complexity and imperfect quality can lead to biased inference. While RWD offer the opportunity to generate timely evidence grounded in real-world populations and clinical practice\, issues of information bias and confounding create serious threats to the validity of these studies. The statistician is faced with a quandary: how to effectively utilize RWD to advance research without compromising best practices for principled data analysis. In this talk I will use examples from my research on methods for the analysis of EHR derived-data to illustrate how an understanding of the EHR data generating mechanism can inform selection of appropriate study questions and development and application of statistical methods that minimize the risk of bias. The overarching goal of this presentation is to raise awareness of challenges associated with the analysis of RWD and demonstrate that valid evidence generation can be grounded in an understanding of the scientific context and data generating process. \n  \nDr. Hubbard’s research focuses on the development and application of methods to improve analyses using real world data sources including electronic health records (EHR) and claims data. The data science era demands novel analytic methods to transform the wealth of data created as a byproduct of our digital interactions into valid and generalizable knowledge. Dr. Hubbard’s research emphasizes statistical methods designed to meet this challenge by addressing the messiness and complexity of real world data including informative observation schemes\, phenotyping error\, and error and missingness in confounders. Her methods have been applied to support the advancement of a broad range of research areas through use of EHR and claims data including health services research\, cancer epidemiology\, aging and dementia\, and pharmacoepidemiology. \n 
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-using-ehr-to-accelerate-cancer-outcomes-research-without-leaving-valid-inference-behind/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/01/1154_3.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220304T120000
DTEND;TZID=America/New_York:20220304T130000
DTSTAMP:20260407T224715
CREATED:20220223T191012Z
LAST-MODIFIED:20220304T195647Z
UID:3365-1646395200-1646398800@ds.dfci.harvard.edu
SUMMARY:Data Science Seminar: Radiomics for Feature Extraction from Radiological Images
DESCRIPTION:Friday\, March 4\, 2022\n12:00PM Eastern Time \nAni Eloyan\, PhD\nAssistant Professor\nDepartment of Biostatistics\, Brown University \nRegister. \nAbstract: Cancer patients routinely undergo radiological evaluations where images of various modalities including computed tomography\, positron emission tomography\, and magnetic resonance images are collected for diagnosis and for evaluation of disease progression. Tumor characteristics\, often referred to as measures of tumor heterogeneity\, can be computed using these clinical images and used as predictors of disease progression and patient survival. Several approaches to quantifying tumor heterogeneity have been proposed including simple intensity histogram-based measures\, metrics attempting to quantify average distance from a homogeneous surface\, and texture analysis-based methods. I will present a statistical framework for principal manifold estimation for obtaining a smooth estimate of tumor surface. I will describe an estimation procedure for tumor heterogeneity using clustering methods considering the neighborhood topology of the tumors. The proposed approach incorporates the spatial structure of the tumor image using neighborhood summary measures. The results of using the proposed methods to estimate cancer survival using machine learning algorithms will be discussed.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-radiomics-for-feature-extraction-from-radiological-images/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/02/aeloyan_photo.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220307T130000
DTEND;TZID=America/New_York:20220307T140000
DTSTAMP:20260407T224715
CREATED:20220301T215910Z
LAST-MODIFIED:20220307T192958Z
UID:3390-1646658000-1646661600@ds.dfci.harvard.edu
SUMMARY:Data Science Seminar: Engineering Protease Activity Sensors For Personalized Detection and Profiling of Cancer
DESCRIPTION:Monday March 7th\, 2022\n1:00PM Eastern Time \nAva Soleimany\, PhD\nSenior Researcher\, Biomedical Machine Learning Group at Microsoft Research\, New England \nAbstract: Precision cancer medicine envisions a world where diagnostic and therapeutic opportunities are intelligently tailored to individual patient needs. Achieving this vision necessitates access to high quality\, accurate\, and individualized information about disease state. Engineered probes that sense disease activity — dynamically and directly within the body — could provide this information by generating signals that functionally measure the state of one’s disease. \nIn this talk\, I will discuss my work in engineering a novel class of nanoscale sensors that directly query tumor microenvironments by measuring the activity of proteases\, enzymes directly involved in all functional hallmarks of cancer. I will share how we can leverage the rich\, functional data generated by these sensors to design and deploy novel\, expressive\, and high-fidelity machine learning models to power individualized diagnostic decision-making and noninvasive monitoring. Finally\, I show how we can engineer sensors to spatially profile protease activity in situ\, demonstrating their ability to discover personalized insights into mechanisms of tumor progression and treatment response and encouraging their translation for precision cancer medicine.
URL:https://ds.dfci.harvard.edu/event/data-science-seminar-engineering-protease-activity-sensors-for-personalized-detection-and-profiling-of-cancer/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2022/03/Ava_Soleimany_Background_small-e1646171916155.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220426T130000
DTEND;TZID=America/New_York:20220426T140000
DTSTAMP:20260407T224715
CREATED:20220105T135143Z
LAST-MODIFIED:20220429T134618Z
UID:3248-1650978000-1650981600@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Tree-based Ensembling Strategies for Handling Heterogeneous Data
DESCRIPTION:Maya Ramchandran\nData Scientist\, ZephyrAI \nAbstract: Adapting machine learning algorithms to better handle clustering or other partition structure within training data sets is important across a wide variety of biological applications. We first consider the task of learning prediction models when multiple training studies are available. We present a novel weighting approach  for constructing tree-based ensemble learners in this setting\, showing that incorporating multiple layers of ensembling in the training process by weighting trees increases the robustness of the resulting predictor and achieves superior performance to Random Forest. Next\, we broaden the scope of the problem to consider the effect of ensembling forest-based learners trained on clusters within a single data set with heterogeneity in the distribution of the features. We show that constructing ensembles of forests trained on estimated clusters determined by algorithms such as k-means results in significant improvements in accuracy and generalizability over the traditional Random Forest algorithm. We denote our novel approach as the Cross-Cluster Weighted Forest\, and display its robustness and accuracy across simulations and on cancer molecular profiling and gene expression data sets that are naturally divisible into clusters. Finally\, we provide theoretical support to these empirical observations by asymptotically analyzing linear least squares and random forest regressions under a linear model. In particular\, for random forest regression under fixed dimensional linear models\, our bounds imply a strict benefit of our ensembling strategy over classic Random Forest. \nYouTube Video. \nMaya Ramchandran recently completed her PhD at the Harvard Biostatistics department under the supervision of Dr. Giovanni Parmigiani\, where she developed machine learning ensembling strategies with applications to cancer prediction problems. She holds a BS in Applied Mathematics-Biology from Brown University and a Masters of Music in Violin Performance from the New England Conservatory. She currently works as a data scientist at ZephyrAI\, a biotechnology startup that develops novel drug combination and repurposing treatments for oncology.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-tree-based-ensembling-strategies-for-handling-heterogeneous-data/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/01/1595653826867.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220913T130000
DTEND;TZID=America/New_York:20220913T140000
DTSTAMP:20260407T224715
CREATED:20220822T141425Z
LAST-MODIFIED:20220916T155058Z
UID:3744-1663074000-1663077600@ds.dfci.harvard.edu
SUMMARY:It’s All Relative: Testing Differential Abundance in Compositional Microbiome Data
DESCRIPTION:Frontiers in Biostatistics Seminar Series \nTuesday September 13\, 2022\n1:00PM Eastern Time \nYijuan Hu\, Ph.D.\nAssociate Professor\nDepartment of Biostatistics and Bioinformatics\nRollins School of Public Health\nEmory University \nRegister for Zoom link \nAbstract: Studies on the human microbiome have revealed that differences in microbial communities are associated with many human disorders such as inflammatory bowel disease\, type II diabetes\, and even Alzheimer’s disease and some cancers. The microbiome is a particularly attractive target for establishing new biomarkers for disease diagnosis and prognosis\, and for developing low-cost\, low-risk interventions. Microbiome data have two unique features. First\, they are compositional\, i.e.\, the total number of sequencing reads per sample is an experimental artifact and only the relative abundance of taxa can be measured. Second\, they are subject to a wide variety of experimental biases (e.g.\, in the process of DNA extraction and PCR amplification) that plague most analyses that directly analyze relative abundance data. These features call for analyses that are based on log-ratio transformation of the relative abundance data. Existing methods often ignore experimental biases\, do not handle the extensive (50-90%) zero count data adequately\, and do not accommodate other complexities in microbiome data (e.g.\, high-dimensionality\, confounding covariates\, and continuous covariates of interest). In this talk\, we present a new logistic-regression-based method that takes into account all of these features of microbiome data for robust testing of differential abundance. Our simulation studies indicate that our method is the only one that universally controls the FDR while at the same time maintaining good power. We illustrate our method by the analysis of a throat microbiome dataset. \n 
URL:https://ds.dfci.harvard.edu/event/its-all-relative-testing-differential-abundance-in-compositional-microbiome-data/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2022/08/hu-photo-small.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221101T130000
DTEND;TZID=America/New_York:20221101T140000
DTSTAMP:20260407T224715
CREATED:20221013T144710Z
LAST-MODIFIED:20221101T194024Z
UID:3821-1667307600-1667311200@ds.dfci.harvard.edu
SUMMARY:Design and Implementation of Bayesian Adaptive Phase I Trials in Oncology using the DEDUCE Application
DESCRIPTION:Frontiers in Biostatistics Seminar Series \nTuesday November 1\, 2022\n1:00PM Eastern Time \nRegister for in-person or virtual attendance. \nWendy London\, PhD\nAssociate Professor\, Harvard Medical School\nDirector of Biostatistics\, Boston Children’s Hospital \nClement Ma\, PhD Assistant Professor\, University of Toronto\nIndependent Scientist\, CAMH
URL:https://ds.dfci.harvard.edu/event/design-and-implementation-of-bayesian-adaptive-phase-i-trials-in-oncology-using-the-deduce-application/
LOCATION:Center of Life Sciences\, Room 11081\, 3 Blackfan Circle\, Boston\, MA\, 02215\, United States
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221206T130000
DTEND;TZID=America/New_York:20221206T140000
DTSTAMP:20260407T224715
CREATED:20221013T170456Z
LAST-MODIFIED:20221207T142413Z
UID:3837-1670331600-1670335200@ds.dfci.harvard.edu
SUMMARY:COVID Data-Driven Policy: NC DHHS's Use of Data for Pandemic Response
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday December 6\, 2022\n1:00PM Eastern Time \nRegister for Zoom link. \nJessie Tenenbaum\, PhD\nshe / her / hers\nChief Data Officer\nNC Department of Health and Human Services
URL:https://ds.dfci.harvard.edu/event/covid-data-driven-policy-nc-dhhss-use-of-data-for-pandemic-response/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2022/10/Tenenbaum-square-copy.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230124T130000
DTEND;TZID=America/New_York:20230124T140000
DTSTAMP:20260407T224715
CREATED:20221128T192916Z
LAST-MODIFIED:20230203T145004Z
UID:3888-1674565200-1674568800@ds.dfci.harvard.edu
SUMMARY:A Nonparametric Bayesian Approach to Use RWD in Clinical Trial Design
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday January 24\, 2023\n1:00PM Eastern Time\nClick to watch the YouTube video. \nPeter Mueller\, PhD\nProfessor\nDepartment of Statistics and Data Sciences\nDepartment of Mathematics\nUniversity of Texas at Austin \n 
URL:https://ds.dfci.harvard.edu/event/a-nonparametric-bayesian-approach-to-use-rwd-in-clinical-trial-design/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2022/11/mueller.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230214T130000
DTEND;TZID=America/New_York:20230214T140000
DTSTAMP:20260407T224715
CREATED:20221128T194757Z
LAST-MODIFIED:20230307T152329Z
UID:3899-1676379600-1676383200@ds.dfci.harvard.edu
SUMMARY:Using the Case Study of Atezolizumab Development to Rethink Early Phase Oncology Trial Design
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday February 14\, 2023\n1:00PM Eastern Time\nYouTube video \nEmily Zabor\, DrPH\nAssistant Staff Biostatistician\nDepartment of Quantitative Health Sciences at the Cleveland Clinic\, with a joint appointment in the Taussig Cancer Institute\nAssistant Professor of Medicine\, the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-emily-zabor/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/11/emily-touched-8-crop-scaled-e1669664843585.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230302T130000
DTEND;TZID=America/New_York:20230302T140000
DTSTAMP:20260407T224715
CREATED:20230224T194353Z
LAST-MODIFIED:20230308T124839Z
UID:4088-1677762000-1677765600@ds.dfci.harvard.edu
SUMMARY:Computation of High-Dimensional Penalized Generalized Linear Mixed Models
DESCRIPTION:Thursday March 2\, 2023\n1:00pm ET \nHillary Heiling\nBiostatistics PhD candidate\nUniversity of North Carolina Chapel Hill. \nAdd to calendar \nHillary’s statistical focus has primarily been in cancer-related research\, both through her graduate research assistant role in the Lineberger Comprehensive Cancer Center as well as her personal research applications. In cancer research as well as other biomedical areas\, there are issues with the replicability of results between studies. Hillary will present her work on improving the replicable selection of gene signatures for the prediction of cancer subtypes by combining data across studies and performing variable selection on generalized linear mixed models. Hillary has been able to extend the feasible dimensionality of the application to hundreds of predictors by using a factor model decomposition on the random effects\, which behaves as a dimension reduction technique. Hillary has developed software to perform this task and has published her ‘glmmPen’ R package on CRAN.
URL:https://ds.dfci.harvard.edu/event/computation-of-high-dimensional-penalized-generalized-linear-mixed-models/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2023/02/hillary-e1677267404626.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230329T130000
DTEND;TZID=America/New_York:20230329T140000
DTSTAMP:20260407T224715
CREATED:20230120T174649Z
LAST-MODIFIED:20230329T184122Z
UID:3949-1680094800-1680098400@ds.dfci.harvard.edu
SUMMARY:Dimension Reduction of Longitudinal Microbiome Data
DESCRIPTION:Frontiers in Biostatistics Seminar \nWednesday March 29\, 2023\n1:00PM Eastern Time \nPixu Shi\, PhD\nAssistant Professor of Biostatistics & Bioinformatics\nDivision of Integrative Genomics\nDepartment of Biostatistics & Bioinformatics\nDuke University School of Medicine \nAbstract:\nThe analysis of longitudinal microbiome is crucial to the understanding of how microbiome changes over time. It often requires careful maneuver of high dimensional microbial features\, missing samples and varying time points across subjects. Longitudinal microbiome data can often be formatted into a high-dimensional order-3 tensor with three modes representing the subject\, time\, and bacteria respectively. In this talk\, we present functional tensor SVD\, a dimension reduction tool that can uncover sub-population structures in subjects\, compress high-dimensional features into low-dimensional trajectories\, and extract shared temporal patterns among features\, all without imputation of missing samples or rounding of time points. We will demonstrate the robust performance of our method through simulations and multiple case studies.
URL:https://ds.dfci.harvard.edu/event/dimension-reduction-of-longitudinal-microbiome-data/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2023/01/Pixu_profile_square.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230509T130000
DTEND;TZID=America/New_York:20230509T140000
DTSTAMP:20260407T224715
CREATED:20230410T125338Z
LAST-MODIFIED:20230515T123922Z
UID:4170-1683637200-1683640800@ds.dfci.harvard.edu
SUMMARY:Model-robust and Efficient Covariate Adjustment for Cluster-randomized Experiments
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday May 9\, 2023\n1:00PM Eastern Time \nJoin the Zoom. \nFan Li\, PhD\nAssistant Professor of Biostatistics\, Yale School of Public Health \nCluster-randomized experiments are increasingly used to evaluate interventions in routine practice conditions\, and researchers often adopt model-based methods with covariate adjustment in the statistical analyses. However\, the validity of model-based covariate adjustment is unclear when the working models are misspecified\, leading to ambiguity of estimands and risk of bias. In this article\, we first adapt two conventional model-based methods\, generalized estimating equations and linear mixed models\, with weighted g-computation to achieve robust inference for cluster-average and individual-average treatment effects. Furthermore\, we propose an efficient estimator for each estimand that allows for flexible covariate adjustment and additionally addresses cluster size variation dependent on treatment assignment and other cluster characteristics. Such cluster size variations often occur post-randomization and\, if ignored\, can lead to bias of model-based estimators. For our proposed estimator\, we prove that when the nuisance functions are consistently estimated by machine learning algorithms\, the estimator is consistent\, asymptotically normal\, and efficient. When the nuisance functions are estimated via parametric working models\, the estimator is triply-robust. Simulation studies and analyses of three real-world cluster-randomized experiments demonstrate that the proposed methods are superior to existing alternatives.
URL:https://ds.dfci.harvard.edu/event/model-robust-and-efficient-covariate-adjustment-for-cluster-randomized-experiments/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2023/04/fanli-scaled-e1681131195600.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230926T100000
DTEND;TZID=America/New_York:20230926T110000
DTSTAMP:20260407T224715
CREATED:20230825T175051Z
LAST-MODIFIED:20231005T140106Z
UID:4458-1695722400-1695726000@ds.dfci.harvard.edu
SUMMARY:Forecasting pancreatic carcinogenesis from spatial multi-omics
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday September 26\, 2023 @ 10am ET\nCenter for Life Sciences Building\, Room 11081 \nElana Fertig\, PhD\nDivision Director of Oncology Quantitative Sciences\, Professor of Oncology\nJohns Hopkins University \nYouTube Video \nCombining genomics with mathematical modeling provides a forecast system that can yield computational predictions to anticipate cancer progression and therapeutic response. High-throughput profiling technologies can indicate the molecular and cellular pathways of malignancies\, but not the effect of targeting those pathways with therapy. Precision interception requires relating therapies to the cellular phenotypes underlying pancreatic carcinogenesis. This talk presents a hybrid computational and experimental strategy to uncover interactions between neoplastic cells and the microenvironment during pancreatic carcinogenesis. As pancreatic cancer develops\, it forms a complex microenvironment of multiple interacting cells. The microenvironment of advanced pancreatic cancer includes a heterogeneous and dense population of cells\, such as macrophages and fibroblasts\, that are associated with immunosuppression. New single-cell and spatial molecular profiling technologies enable unprecedented characterization of the cellular and molecular composition of the microenvironment. These technologies provide the potential to identify candidate therapeutics to intercept immunosuppression in pancreatic cancer. State-of-the-art mathematical approaches in computational biology are essential to uncover mechanistic insights for high-throughput data for these precision interception strategies. \n  \nWant to get our weekly events newsletter? Click here!
URL:https://ds.dfci.harvard.edu/event/forecasting-pancreatic-carcinogenesis-from-spatial-multi-omics/
LOCATION:Center of Life Sciences\, Room 11081\, 3 Blackfan Circle\, Boston\, MA\, 02215\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2023/08/headshot-copy.jpg
GEO:42.3394159;-71.104234
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Center of Life Sciences Room 11081 3 Blackfan Circle Boston MA 02215 United States;X-APPLE-RADIUS=500;X-TITLE=3 Blackfan Circle:geo:-71.104234,42.3394159
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231024T130000
DTEND;TZID=America/New_York:20231024T140000
DTSTAMP:20260407T224715
CREATED:20230825T175528Z
LAST-MODIFIED:20231031T115135Z
UID:4463-1698152400-1698156000@ds.dfci.harvard.edu
SUMMARY:AI in Medical Imaging: Current State & Future Opportunities
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday October 24\, 2023 @ 1pm ET\nCenter for Life Sciences Building\, Room 11081 \nWilliam Lotter\, PhD\nAssistant Professor\, Dana-Farber Cancer Institute and Harvard Medical School \nYouTube Video \nWant to get our weekly events newsletter? Click here!
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-william-lotter/
LOCATION:Center of Life Sciences\, Room 11081\, 3 Blackfan Circle\, Boston\, MA\, 02215\, United States
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2022/08/Bill_Lotter_headshot-scaled-e1659653586370.jpg
GEO:42.3394159;-71.104234
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231108T120000
DTEND;TZID=America/New_York:20231108T130000
DTSTAMP:20260407T224715
CREATED:20231030T131716Z
LAST-MODIFIED:20231030T131847Z
UID:4616-1699444800-1699448400@ds.dfci.harvard.edu
SUMMARY:Single-cell Multiomic Exploration of Oncogenic and Immunologic Programs in Melanoma and CLL
DESCRIPTION:Elevate @ Eleven: Comp Bio Connections \nWednesday November 8th\, 2023\n12:00-1:00PM\nIn-person only\, Zelen Commons\, 11th floor of the Center for Life Sciences Building \nJeremy Simon\nSenior Research Scientist\, Harvard T.H. Chan School of Public Health \nLunch is provided. \n 
URL:https://ds.dfci.harvard.edu/event/single-cell-multiomic-exploration-of-oncogenic-and-immunologic-programs-in-melanoma-and-cll/
LOCATION:Center for Life Sciences\, Zelen Commons\, 3 Blackfan Circle\, Boston\, MA\, 02115
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2023/01/Jeremy-Simon-600x600-1-e1673981068499.jpeg
GEO:42.3394159;-71.104234
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Center for Life Sciences Zelen Commons 3 Blackfan Circle Boston MA 02115;X-APPLE-RADIUS=500;X-TITLE=3 Blackfan Circle:geo:-71.104234,42.3394159
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231205T130000
DTEND;TZID=America/New_York:20231205T140000
DTSTAMP:20260407T224715
CREATED:20230901T114137Z
LAST-MODIFIED:20231208T201749Z
UID:4488-1701781200-1701784800@ds.dfci.harvard.edu
SUMMARY:Analyzing Big EHR Data - Optimal Cox Regression Subsampling Procedure with Rare Events
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday December 5\, 2023 @ 1pm ET\nYouTube Link \nMalka Gorfine\, PhD\nProfessor\, Department of Statistics\,  Tel Aviv University\, Israel \nAbstract: Massive sized survival datasets become increasingly prevalent with the development of the healthcare industry\, and pose computational challenges unprecedented in traditional survival analysis use cases. In this work we analyze the UK-biobank colorectal cancer data with genetic and environmental risk factors\, including a time-dependent coefficient\, which transforms the dataset into “pseudo-observation” form\, thus critically inflating its size. A popular way for coping with massive datasets is downsampling them\, such that the computational resources can be afforded by the researcher. Cox regression has remained one of the most popular statistical models for the analysis of survival data to-date. This work addresses the settings of right censored and possibly left-truncated data with rare events\, such that the observed failure times constitute only a small portion of the overall sample. We propose Cox regression subsampling-based estimators that approximate their full-data partial-likelihood-based counterparts\, by assigning optimal sampling probabilities to censored observations\, and including all observed failures in the analysis. The suggested methodology is applied on the UK-biobank for building a colorectal cancer risk-prediction model\, while reducing the computation time and memory requirements. We establish asymptotic properties under suitable conditions and develop a framework for determining the optimal subsample size. \nWant to get our weekly events newsletter? Click here!
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-seminar-malka-gorfine/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2023/09/portrait.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20231206T120000
DTEND;TZID=America/New_York:20231206T130000
DTSTAMP:20260407T224715
CREATED:20231121T123117Z
LAST-MODIFIED:20231121T123117Z
UID:4661-1701864000-1701867600@ds.dfci.harvard.edu
SUMMARY:The Splicing Factor CCAR1 Regulates the Fanconi Anemia/BRA Pathway
DESCRIPTION:Elevate @ Eleven: Comp Bio Connections \nWednesday December 6th\, 2023\n12:00-1:00PM\nIn-person only\, Zelen Commons\, 11th floor of the Center for Life Sciences Building \nHuy Nguyen\nComputational Biologist\, Center for DNA Damage & Repair\, Dana-Farber Cancer Institute \nLunch is provided. \n 
URL:https://ds.dfci.harvard.edu/event/the-splicing-factor-ccar1-regulates-the-fanconi-anemia-bra-pathway/
LOCATION:Center for Life Sciences\, Zelen Commons\, 3 Blackfan Circle\, Boston\, MA\, 02115
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2023/11/huy_square.png
GEO:42.3394159;-71.104234
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Center for Life Sciences Zelen Commons 3 Blackfan Circle Boston MA 02115;X-APPLE-RADIUS=500;X-TITLE=3 Blackfan Circle:geo:-71.104234,42.3394159
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240110T120000
DTEND;TZID=America/New_York:20240110T130000
DTSTAMP:20260407T224715
CREATED:20240104T202615Z
LAST-MODIFIED:20240104T202615Z
UID:4773-1704888000-1704891600@ds.dfci.harvard.edu
SUMMARY:ImmunoPROFILE: An Multiplex Immunofluorescence Based Immune Cell Profiling Test and Data Resource; An Overview and Analysis Vignette Using GNNs
DESCRIPTION:Elevate @ Eleven: Comp Bio Connections \nWednesday January 10th\, 2023\n12:00-1:00PM\nIn-person only\, Zelen Commons\, 11th floor of the Center for Life Sciences Building \nKatharina Hoebel\, Research Fellow\, Department of Data Science\, Dana-Farber Cancer Institute\nJames Lindsay\, Director\, Software Engineer\, Department of Data Science\, Dana-Farber Cancer Institute \nLunch is provided.
URL:https://ds.dfci.harvard.edu/event/immunoprofile-an-multiplex-immunofluorescence-based-immune-cell-profiling-test-and-data-resource-an-overview-and-analysis-vignette-using-gnns/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2024/01/katharina_james2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240116T130000
DTEND;TZID=America/New_York:20240116T140000
DTSTAMP:20260407T224715
CREATED:20230927T113715Z
LAST-MODIFIED:20240119T180817Z
UID:4572-1705410000-1705413600@ds.dfci.harvard.edu
SUMMARY:Methods for the Analysis of Data with Missing Values
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday January 16\, 2023 @ 1pm ET\nCenter for Life Sciences Building\, Room 11081 \nRoderick Little\, PhD\nRichard D. Remington Distinguished University Professor of Biostatistics\nProfessor\, Department of Statistics\nResearch Professor\, Institute for Social Research\nUniversity of Michigan School of Public Health \nYouTube Video \nAbstract: I review methods for handling missing data in empirical studies. I define missing data\, provide a taxonomy of main approaches to analysis\, including complete-case and available-case analysis\, weighting\, maximum likelihood (ML)\, Bayes\, single and multiple imputation (MI)\, and augmented inverse-probability weighting (AIPW). Assumptions about the missingness mechanism are key to the performance of alternative methods; I define missingness mechanisms\, which play a key role in the performance of methods. Approaches to robust inference\, and to inference when the mechanism is potentially missing not at random\, are discussed. I’ll also discuss recent developments in the treatment of missing data in interventional studies\, as evidenced by the International Council for Harmonization E9 Addendum on estimands. \nWant to get our weekly events newsletter? Click here!
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-seminar-roderick-little/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2023/09/rlittle_crop.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240124T120000
DTEND;TZID=America/New_York:20240124T130000
DTSTAMP:20260407T224715
CREATED:20240111T154411Z
LAST-MODIFIED:20240201T125325Z
UID:4827-1706097600-1706101200@ds.dfci.harvard.edu
SUMMARY:Tracing the Mutational Footprints of Cancer to Guide Personalized Therapy
DESCRIPTION:Elevate @ Eleven: Comp Bio Connections \nWednesday January 24th\, 2023\n12:00-1:00PM\nIn-person only\, Zelen Commons\, 11th floor of the Center for Life Sciences Building \nDoğa Gülhan\, Principal Investigator\, Mass General Cancer Center\, KF-CCR \nLunch is provided.
URL:https://ds.dfci.harvard.edu/event/tracing-the-mutational-footprints-of-cancer-to-guide-personalized-therapy/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2024/01/gulhan-square.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240206T130000
DTEND;TZID=America/New_York:20240206T140000
DTSTAMP:20260407T224715
CREATED:20240126T132133Z
LAST-MODIFIED:20240209T201454Z
UID:4874-1707224400-1707228000@ds.dfci.harvard.edu
SUMMARY:Quantitative Methods in Implementation Research: Concepts\, Goals\, and Applications
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday February 6\, 2024 @ 1pm ET\nCenter for Life Sciences Building\, Room 11081 \nYou Tube Link \nDonna Spiegelman\, ScD\nSusan Dwight Bliss Professor of Biostatistics and Professor of Cardiovascular Medicine\, Yale School of Medicine Professor\, Department of Statistics and Data Science\, Yale University \nThis talk will provide an overview of the key concepts and goals of quantitative methods that are popular in implementation research\, with a wide range of real-world applications. Randomized\, quasi-experimental and observational designs will be covered.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-donna-spiegelman/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2024/01/spiegelman-crop.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240207T120000
DTEND;TZID=America/New_York:20240207T130000
DTSTAMP:20260407T224715
CREATED:20240129T134117Z
LAST-MODIFIED:20240209T142125Z
UID:4891-1707307200-1707310800@ds.dfci.harvard.edu
SUMMARY:Reconstructing the Chronology of Somatic Events From Precursor Conditions to Advanced Stage Myeloma
DESCRIPTION:Elevate @ Eleven: Comp Bio Connections \nWednesday February 7th\, 2024\n12:00-1:00PM\nIn-person only\, Zelen Commons\, 11th floor of the Center for Life Sciences Building \nMehmet Samur\, Senior Research Scientist\, Department of Data Science \nLunch is provided.
URL:https://ds.dfci.harvard.edu/event/reconstructing-the-chronology-of-somatic-events-from-precursor-conditions-to-advanced-stage-myeloma/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/04/samur_mehmet.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240301
DTEND;VALUE=DATE:20240302
DTSTAMP:20260407T224715
CREATED:20240209T142504Z
LAST-MODIFIED:20240304T124147Z
UID:4928-1709251200-1709337599@ds.dfci.harvard.edu
SUMMARY:Call for Abstracts: Health Disparities and Cancer
DESCRIPTION:We invite students\, postdocs\, residents\, clinical fellows\, and early career faculty to submit abstracts for consideration as a lightning talk at the 2024 Marvin Zelen Memorial Symposium. Submit via this form by Friday March 1st\, speakers will be notified by Friday March 8th. \n  \nFriday April 5\, 2024\n1:00-5:30PM ET \nYawkey Conference Center\n450 Brookline Ave\, Boston\, MA \nRegistration for in person and virtual attendance. \nInvited speakers: \n\nAdewole Adamson\, MD\, MPP\, Dell Medical School at the University of Texas at Austin\nCynthia Dwork\,PhD\, Harvard University\nNate Fillmore\,PhD\, VA Boston Healthcare System and Harvard Medical School\nCarmen E. Guerra\, MD\, MSCE\, University of Pennsylvania\nJinani Jayasekera\, PhD\, National Institute on Minority Health and Health Disparities\nBogdan Pasaniuc\,PhD\, University of California Los Angeles\n\n 
URL:https://ds.dfci.harvard.edu/event/call-for-abstracts-health-disparities-and-cancer/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2024/02/Zelen2024_forwebsite_image.png
END:VEVENT
END:VCALENDAR