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DTSTART;TZID=America/New_York:20220228T130000
DTEND;TZID=America/New_York:20220228T140000
DTSTAMP:20260409T010036
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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/
LOCATION:MA
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:20220224T130000
DTEND;TZID=America/New_York:20220224T140000
DTSTAMP:20260409T010036
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/
LOCATION:MA
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:20220215T130000
DTEND;TZID=America/New_York:20220215T140000
DTSTAMP:20260409T010036
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/
LOCATION:MA
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:20220208T130000
DTEND;TZID=America/New_York:20220208T140000
DTSTAMP:20260409T010036
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/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/06/headshot_sq.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220204T130000
DTEND;TZID=America/New_York:20220204T140000
DTSTAMP:20260409T010036
CREATED:20220202T172751Z
LAST-MODIFIED:20220202T172751Z
UID:3303-1643979600-1643983200@ds.dfci.harvard.edu
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/
LOCATION:MA
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:20220111T130000
DTEND;TZID=America/New_York:20220111T140000
DTSTAMP:20260409T010036
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/
LOCATION:MA
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:20211019T130000
DTEND;TZID=America/New_York:20211019T140000
DTSTAMP:20260409T010036
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/
LOCATION:MA
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:20210914T130000
DTEND;TZID=America/New_York:20210914T140000
DTSTAMP:20260409T010036
CREATED:20210614T164652Z
LAST-MODIFIED:20211021T134349Z
UID:2797-1631624400-1631628000@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: COVID Vaccine Efficacy Trial Designs\, Open Questions and Statistical Challenges
DESCRIPTION:Tuesday September 14\, 2021\n1:00PM Eastern Time \nHolly Janes\, Ph.D.\nProfessor\, Vaccine and Infectious Disease Division\, Fred Hutchinson Cancer Research Center\nProfessor\, Public Health Sciences Division\, Fred Hutchinson Cancer Research Center \nYouTube video now available.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-holly-janes/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/06/holly-janes-sq.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210608T130000
DTEND;TZID=America/New_York:20210608T140000
DTSTAMP:20260409T010036
CREATED:20210108T131835Z
LAST-MODIFIED:20211021T134536Z
UID:2398-1623157200-1623160800@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: The Use of External Control Data for Predictions and Interim Analyses in Clinical Trials
DESCRIPTION:June 8\, 2021\n1:00PM \nLorenzo Trippa\, PhD\nAssociate Professor\nDepartment of Biostatistics\, Harvard T.H. Chan School of Public Health\nDepartment of Data Science\, Dana-Farber Cancer Institute \nThe Use of External Control Data for Predictions and Interim Analyses in Clinical Trials \nYouTube video now available.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-lorenzo-trippa/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2021/01/trippa.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210511T130000
DTEND;TZID=America/New_York:20210511T140000
DTSTAMP:20260409T010036
CREATED:20200921T180944Z
LAST-MODIFIED:20210511T180948Z
UID:2138-1620738000-1620741600@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Single-Cell RNA-Seq Data Analysis Via a Regularized Zero-Inflated Mixture Model Framework
DESCRIPTION:Frontiers in Biostatistics Seminar\nMay 11\, 2021\n1:00PM \nJianhua Hu\, PhD\nProfessor\, Biostatistics (in Medicine and in the Herbert Irving Comprehensive Cancer Center)\nDirector\, Cancer Biostatistics Program\nColumbia University \nRegister at: http://bit.ly/FIBMay21 \nAbstract: Applications of single-cell RNA sequencing in various biomedical research areas have been blooming. This new technology provides unprecedented opportunities to study disease heterogeneity at the cellular level. However\, unique characteristics of scRNA-seq data\, including large dimensionality\, high dropout rates\, and possibly batch effects\, bring great difficulty into the analysis of such data. Not appropriately addressing these issues obstructs true scientific discovery. Herein\, we propose a unified Regularized Zero-inflated Mixture Model framework designed for scRNA-seq data (RZiMM-scRNA) to simultaneously detect cell subgroups and identify gene differential expression based on a developed importance score\, accounting for both dropouts and batch effects. We conduct extensive empirical investigation to demonstrate the promise of RZiMM-scRNA in comparison to several popular methods\, including K-means and Hierarchical clustering. \nThis seminar was not recorded as the research presented has not been published yet. 
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-jianhua-hu/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/09/Hu_4x4.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210309T130000
DTEND;TZID=America/New_York:20210309T140000
DTSTAMP:20260409T010036
CREATED:20200921T180427Z
LAST-MODIFIED:20210511T181203Z
UID:2133-1615294800-1615298400@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Distributed Statistical Learning and Inference in EHR and Other Healthcare Datasets
DESCRIPTION:Frontiers in Biostatistics Seminar\nMarch 9\, 2021\n1:00PM \nRui Duan\, PhD\nAssistant Professor of Biostatistics\nHarvard TH Chan School of Public Health \nDistributed Statistical Learning and Inference in EHR and Other Healthcare Datasets  \nAbstract: The growth of availability and variety of healthcare data sources has provided unique opportunities for data integration and evidence synthesis\, which can potentially accelerate knowledge discovery and enable better clinical decision making.  However\, many practical and technical challenges\, such as data privacy\, high-dimensionality and heterogeneity across different datasets\, remain to be addressed. In this talk\, I will introduce several methods for effective and efficient integration of electronic health records and other healthcare datasets. Specifically\, we develop communication-efficient distributed algorithms for jointly analyzing multiple datasets without the need of sharing patient-level data. Our algorithms are able to account for heterogeneity across different datasets. We provide theoretical guarantees for the performance of our algorithms\, and examples of implementing the algorithms to real-world clinical research networks. \nYouTube Link: https://www.youtube.com/watch?v=IscQ3ruxl1o
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-rui-duan/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/09/duan4x4.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210209T130000
DTEND;TZID=America/New_York:20210209T140000
DTSTAMP:20260409T010036
CREATED:20200921T175912Z
LAST-MODIFIED:20210511T181321Z
UID:2129-1612875600-1612879200@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Group Sequential Design Assuming Delayed Benefit
DESCRIPTION:February 9\, 2021\n1:00PM ET \nKeaven Anderson\, PhD\nScientific AVP\, Methodology Research\, Biostatistics at Merck \nGroup Sequential Design Assuming Delayed Benefit \nAbstract: 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 survival. The asymptotic approach allows both quick derivation of study design properties that are also easily verified using simulation. We rely heavily on work done by Tsiatis\, 1981 published while he was at the HSPH. The impact of a potential delay in treatment effect on timing of analyses\, study boundaries and sample size are demonstrated. The value of a robust design that is well powered under a variety of assumptions is emphasized. Open source software is provided for implementation. Given the current regulatory climate\, logrank testing may still be preferred for these trials\, but we hope that efficiencies and Type I error control associated with alternatives may make other options more acceptable for future consideration. \nYouTube Link: https://www.youtube.com/watch?v=DhwZOX5uMKU
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-keaven-anderson/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/09/anderson1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210112T130000
DTEND;TZID=America/New_York:20210112T140000
DTSTAMP:20260409T010036
CREATED:20200921T175311Z
LAST-MODIFIED:20210112T190625Z
UID:2123-1610456400-1610460000@ds.dfci.harvard.edu
SUMMARY:Frontiers in Biostatistics: Statistical Modeling and Adjustment for Sampling Biases
DESCRIPTION:Frontiers in Biostatistics Seminar\nJanuary 12\, 2021\n1:00PM \nJing Ning\, PhD\nAssociate Professor. Department of Biostatistics\nDivision of Quantitative Sciences\nThe University of Texas M.D. Anderson Cancer Center \nRegister at: https://bit.ly/FIBJan12 \nAbstract: Bias sampling mechanisms are commonly encountered in applications where the subjects in a target population are not given an equal chance to be selected\, either accidentally\, by natural circumstances\, or intentionally by design. Statistical methods not properly accounting for such a challenge often lead to invalid inferences. For example\, evidence combined from published studies may lead to overly optimistic conclusions due to publication bias\, and the well-known length bias can cause the screening to appear to be more successful than it really is. In this talk\, I will present our recent work to adjust the sampling biases in diverse applications such as the survivorship bias in prevalent cohort\, the self-reporting bias in longitudinal analysis and the publication bias in meta-analysis.
URL:https://ds.dfci.harvard.edu/event/frontiers-in-biostatistics-jing-ning/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/09/ning.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201214T150000
DTEND;TZID=America/New_York:20201214T163000
DTSTAMP:20260409T010036
CREATED:20201130T190048Z
LAST-MODIFIED:20201215T114236Z
UID:2341-1607958000-1607963400@ds.dfci.harvard.edu
SUMMARY:DF/HCC Cancer Data Science Program &Harvard Chan Bioinformatics CoreJoint Symposium on scRNAseq Methodology
DESCRIPTION:Monday\, December 14\, 3:00-4:30 PM ET  \nRSVP https://bit.ly/CDSBioDec14 \nSpeakers: \n\nAedin Culhane\, Senior Research Scientist\, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health\nIsabella Grabski\, PhD Student in Biostatistics\, Harvard University\nProbabilistic gene barcodes identify cell-types in single-cell RNA-sequencing data\nShannan Ho Sui\, Senior Research Scientist\, Harvard T.H. Chan School of Public Health\nRadhika S Khetani\, Research Scientist\, Harvard T.H. Chan School of Public Health\nPeter Kharchenko\, Associate Professor of Biomedical Informatics\, Harvard Medical School\nX. Shirley Liu\, Professor\, Harvard T.H. Chan School of Public Health\nAnalysis of Single-Cell Data to Model Tumor and Immune Gene Regulation\nLuca Pinello\, Associate Professor of Pathology\, Harvard Medical School and Mass General Cancer Center\nTrajectory inference and visualization of single cell data
URL:https://ds.dfci.harvard.edu/event/df-hcc-cancer-data-science-program-harvard-chan-bioinformatics-corejoint-symposium-on-scrnaseq-methodology/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/11/dfhhc-joint-symp-icon.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201015T130000
DTEND;TZID=America/New_York:20201015T140000
DTSTAMP:20260409T010036
CREATED:20201008T172433Z
LAST-MODIFIED:20201019T155448Z
UID:2174-1602766800-1602770400@ds.dfci.harvard.edu
SUMMARY:Computational Biology of DNA Repair in Cancer
DESCRIPTION:Data Science Seminar \nOctober 15\, 2020\n1:00PM ET \nDominik Glodzik\, PhD\nRepare Therapeutics \nZoom link: https://bit.ly/DSOct15 \nAbstract: 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 progression of cancer. \nWe extended the computational methods for analysis of mutation signatures in order to describe patterns of chromosomal rearrangements. In particular\, the rearrangement signatures enable the assessment of proficiency of homologous recombination (HR). HRDetect\, an algorithm we developed\, predicts probability of HR-deficiency\, and is based on holistic portrayal of mutational signatures across different classes of somatic mutations. Around 20% of breast cancers contain signatures of HR-deficiency\, and this group is wider than the group of carriers of BRCA1/2 mutations. By contrast to adult cancers\, pediatric cancers with known DNA-repair defects display variation of mutational signatures\, hinting at tissue-specificity of mutational signatures. Finally\, in the chromosomally unstable cancers\, we identified structural rearrangements\, in coding and non-coding regions\, that can act as cancer drivers. Altogether\, these results indicate that computational assessment of DNA-repair capacity of tumor cells is now possible. The methods will be crucial to understanding of the DNA-repair mechanisms and tissue-specificity of mutational processes. \nBio: Dominik Glodzik received his PhD in Computational Biology from the University of Edinburgh\, and held a postdoctoral position at Wellcome Trust Sanger Institute\, before moving to a staff scientist position at Memorial Sloan Kettering Cancer Center. Currently he is a Principal Bioinformatician at Repare Therapeutics in Cambridge.
URL:https://ds.dfci.harvard.edu/event/computational-biology-of-dna-repair-in-cancer/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/10/glodzik.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201013T130000
DTEND;TZID=America/New_York:20201013T140000
DTSTAMP:20260409T010036
CREATED:20200430T165238Z
LAST-MODIFIED:20201109T234833Z
UID:894-1602594000-1602597600@ds.dfci.harvard.edu
SUMMARY:Constructing Confidence Interval for RMST under Group Sequential Setting
DESCRIPTION:Frontiers in Biostatistics Seminar\nOctober 13\, 2020\n1:00PM \nLu Tian\, PhD\nAssociate Professor of Biomedical Data Science in the School of Medicine\nStanford University \nIt 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 assuming restrictive model assumptions such as the proportional hazards assumption. However\, there are special challenges in designing and analyzing group sequential study based on RMST\, because the truncation timepoint of the RMST in the interim analysis often differs from that in the final analysis. A valid test controls the unconditional type one error has been developed in the past. However\, there is no appropriate statistical procedure for constructing the confidence interval for the treatment effect measured by a contrast in RMST\, while it is crucial for informative clinical decision making. In this talk\, I will review some important design issues for study based on RMST. I will then discuss how to conduct hypothesis testing and how to construct confidence intervals for the difference RMST in a group sequential setting. Examples and numerical studies will be presented to illustrate the method. \nA recording of this seminar is available on your YouTube Channel.
URL:https://ds.dfci.harvard.edu/event/constructing-confidence-interval-for-rmst-under-group-sequential-setting/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/04/lutian.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200929T130000
DTEND;TZID=America/New_York:20200929T140000
DTSTAMP:20260409T010036
CREATED:20200922T173720Z
LAST-MODIFIED:20200923T183848Z
UID:2146-1601384400-1601388000@ds.dfci.harvard.edu
SUMMARY:3D Spatial Organization Within Tumors
DESCRIPTION:Data Science Seminar \nSeptember 29\, 2020\n1:00PM ET \nMartin Aryee\, PhD\nAssistant Professor of Pathology\, Harvard Medical School\nAssistant Molecular Pathologist\, Massachusetts General Hospital \nZoom link: https://dfci.zoom.us/j/95524743149?pwd=SzN4cjJZUnhsNVl3dXNmZjZ1N3F4QT09 \nAbstract: 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 in a tissue to individual genes and regulatory elements in a single genome. High-throughput assays that permit spatial measurements have advanced greatly in the past decade and revealed oncogenic architectural alterations in tumors at the tissue\, cellular and chromosome levels. Here I will discuss tumor spatial organization in gastrointestinal malignancies at two different scales. First\, I will describe how we used single-cell RNA-Seq and RNA in situ hybridization in pancreatic cancer to identify cell state and tissue architecture changes induced by contact with stromal cancer-associated fibroblasts (CAFs). We analyzed the spatial architecture of cell states in 195 pancreatic tumors\, mapping over 300\,000 individual cancer cells. We were able to classify different types of tumor glands based on their cell-type composition\, and show that these functional units can be used to characterize tumors in ways not evident from analyses of dissociated single cells. Second\, I will discuss findings from a recent study of 3D genome organization within colon cancer cell nuclei. We found oncogenic changes at the level of regulatory chromatin loops and topologically associated domains (TADs)\, but the most surprising and striking change involves a large-scale repackaging of heterochromatin that appears to restrain tumor progression\, representing a failed anti-tumor epigenetic brake.
URL:https://ds.dfci.harvard.edu/event/3d-spatial-organization-within-tumors/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/09/aryee.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200922T140000
DTEND;TZID=America/New_York:20200922T150000
DTSTAMP:20260409T010036
CREATED:20200915T212158Z
LAST-MODIFIED:20200917T142852Z
UID:2101-1600783200-1600786800@ds.dfci.harvard.edu
SUMMARY:Cancer Development\, Heterogeneity and Dynamics from Premalignancy to Drug Refractory Disease
DESCRIPTION:Data Science Seminar \nSeptember 22\, 2020\n2:00PM ET \nIgnaty Leshchiner\, PhD\nPostdoctoral Fellow\, Harvard Medical School/Brigham and Women’s Hospital \nZoom link: http://bit.ly/DSSept22 \nAbstract: \nReal-time study of tumor emergence and progression in patients will help predict and ultimately change the course of the patient’s disease. This could be achieved by inferring genotypes of heterogeneous cell populations within the tumor\, their fitness\, growth rates\, corresponding expression patterns and drug tolerance states. We have developed a set of computational methods to infer the order of tumor-initiating events and to follow the dynamics and competition of cancer cell populations during disease progression and treatment. The package\, PhylogicNDT\, uses tumor genomic data to reconstruct the process of tumor formation\, natural growth kinetics\, competition and spread of resistance clones. We applied this package to 2\,658 primary cancers to reconstruct developmental trajectories and history of common tumor types in premalignancy and early malignancy state; reconstruct cancer cell populations and growth rates\, fitness and kinetics of individual clones during natural progression of leukemia in vivo; analyze spatial progression of resistance clones and find new resistance mechanisms in a large cohort of rapid autopsy cases. By integrating blood biopsy (ctDNA)\, solid tissue biopsy and autopsy data we show that resistance often emerges in multiple distant metastatic sites simultaneously\, with evidence of multiple resistance mutations present in the blood’s ctDNA at the same time. Finally\, we combine bulk and single cell sequencing data to help identify genetically distinct clones and explain their phenotypic differences. We envision that treatment decisions will improve with better understanding of tumor development\, clonal structure and microenvironment\, and the path tumor takes to become malignant and progress after treatment.
URL:https://ds.dfci.harvard.edu/event/cancer-development-heterogeneity-and-dynamics-from-premalignancy-to-drug-refractory-disease/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/09/10221_Facebook_360x360.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200915T130000
DTEND;TZID=America/New_York:20200915T140000
DTSTAMP:20260409T010036
CREATED:20200708T162715Z
LAST-MODIFIED:20201109T234809Z
UID:1804-1600174800-1600178400@ds.dfci.harvard.edu
SUMMARY:A New Hybrid Phase I-II-III Clinical Trial Paradigm
DESCRIPTION:Frontiers in Biostatistics Seminar \nTuesday September 15\, 2020 at 1:00PM Eastern Time \nPeter F. Thall\, PhD\nDepartment of Biostatistics\nUniversity of Texas M.D. Anderson Cancer Center \nAbstract: Conventional evaluation of a new drug\, 𝐴\, is done in three phases. Phase I relies on toxicity to determine a “maximum tolerable dose” (MTD) of 𝐴\, in phase II it is decided whether 𝐴 at the MTD is “promising” in terms of response probability\, and if so a large randomized phase III trial is conducted to compare 𝐴 to a control treatment\, 𝐶\, based on survival time or progression free survival time. This paradigm has many flaws. The first two phases may be combined by conducting a phase I-II trial\, which chooses an optimal dose based on both efficacy and toxicity\, with evaluation of 𝐴 at the optimal phase I-II dose then done in phase III. In this talk\, I will describe a new paradigm\, motivated by the possibility that the optimal phase I-II dose may not maximize mean survival time with 𝐴. A hybrid phase I-II-III design is presented that allows the optimal phase I-II dose of 𝐴 to be re-optimized based on survival time data after the first stage of phase III. The hybrid design relies on a mixture model for the survival time distribution as a function of efficacy\, toxicity\, and dose. A simulation study is presented to evaluate the design’s properties\, including comparison to the more conventional approach that does not re-optimize the dose of 𝐴 in phase III. \nA recording of this seminar is available on your YouTube Channel.
URL:https://ds.dfci.harvard.edu/event/a-new-hybrid-phase-i-ii-iii-clinical-trial-paradigm/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/07/thall_peter.png
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200526T130000
DTEND;TZID=America/New_York:20200526T140000
DTSTAMP:20260409T010036
CREATED:20200525T114005Z
LAST-MODIFIED:20201109T234232Z
UID:1459-1590498000-1590501600@ds.dfci.harvard.edu
SUMMARY:Data Science Zoominar: Teaching Data Science to the Masses
DESCRIPTION:A conversation with Jeff Leek\, PhD\, Johns Hopkins University. \nModerator: Rafael Irizarry. \nRegistration required. \nhttps://dfci.zoom.us/webinar/register/WN_XscX-d21RqylhDCXzvP2-Q \nA recording of the talk is available on our YouTube channel.
URL:https://ds.dfci.harvard.edu/event/data-science-zoominar-teaching-data-science-to-the-masses/
LOCATION:MA
CATEGORIES:Seminar,Zoominar
ATTACH;FMTTYPE=image/jpeg:https://ds.dfci.harvard.edu/wp-content/uploads/2020/05/leek-crop-e1590407126153.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200519T130000
DTEND;TZID=America/New_York:20200519T140000
DTSTAMP:20260409T010036
CREATED:20200429T173724Z
LAST-MODIFIED:20200915T200724Z
UID:667-1589893200-1589896800@ds.dfci.harvard.edu
SUMMARY:Streamlined empirical Bayes estimation for contextual bandits with applications in mobile health
DESCRIPTION:Frontiers in Biostatistics Webinar\nMarianne Menictas\nPostdoctoral Fellow\, Department of Statistics\nHarvard University \nMobile health (mHealth) technologies are increasingly being employed to deliver interventions to users in their natural environments. With the advent of increasingly sophisticated sensing devices (e.g.\, GPS) and phone-based EMA\, it is becoming possible to deliver interventions at moments when they can most readily influence a person’s behavior. For example\, for someone trying to increase physical activity\, moments when the person can be active are critical decision points when a well-timed intervention could make a difference. The promise of mHealth hinges on the ability to provide interventions at times when users need the support and are receptive to it. Thus\, our goal is to learn the optimal time and intervention for a given user and context. A significant challenge to learning is that there are often only a few opportunities per day to provide treatment. Additionally\, when there is limited time to engage users\, a slow learning rate can pose problems\, potentially raising the risk that users will abandon the intervention. To prevent disengagement\, a learning algorithm should learn quickly in spite of noisy measurements. To accelerate learning\, information may be pooled across users and time in a dynamic manner\, combining a contextual bandit algorithm with a Bayesian random effects model for the reward function. As information accumulates\, however\, tuning user and time specific hyperparameters becomes computationally intractable. In this talk\, we focus on solving this computational bottleneck.
URL:https://ds.dfci.harvard.edu/event/streamlined-hyper-parameter-tuning-in-mobile-health/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/04/marianne_uts1_crop.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200428T130000
DTEND;TZID=America/New_York:20200428T140000
DTSTAMP:20260409T010036
CREATED:20200428T180127Z
LAST-MODIFIED:20220525T113511Z
UID:520-1588078800-1588082400@ds.dfci.harvard.edu
SUMMARY:Reframing proportional-hazards modeling for large time-to-event datasets with applications to deep learning
DESCRIPTION:Frontiers in Biostatistics Seminar\nNoah Simon\, Ph.D.\nAssociate Professor\nDepartment of Biostatistics\nUniversity of Washington\n\nTo build inferential or predictive survival models\, it is common to assume proportionality of hazards and fit a model by maximizing the partial likelihood. This has been combined with non-parametric and high dimensional techniques\, eg. spline expansions and penalties\, to flexibly build survival models. \nNew challenges require extension and modification of that approach. In a number of modern applications there is interest in using complex features such as images to predict survival. In these cases\, it is necessary to connect more modern backends to the partial likelihood (such as deep learning infrastructures based on eg. convolutional/recurrent neural networks). In such scenarios\, large numbers of observations are needed to train the model. However\, in cases where those observations are available\, the structure of the partial likelihood makes optimization difficult (if not completely intractable). \nIn this talk we show how the partial likelihood can be simply modified to easily deal with large amounts of data. In particular\, with this modification\, stochastic gradient-based methods\, commonly applied in deep learning\, are simple to employ. This simplicity holds even in the presence of left truncation/right censoring\, and time-varying covariates. This can also be applied relatively simply with data stored in a distributed manner.
URL:https://ds.dfci.harvard.edu/event/reframing-proportional-hazards-modeling-for-large-time-to-event-datasets-with-applications-to-deep-learning/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/04/simon_square.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20200402T130000
DTEND;TZID=America/New_York:20200402T140000
DTSTAMP:20260409T010036
CREATED:20200612T115313Z
LAST-MODIFIED:20200612T122256Z
UID:1618-1585832400-1585836000@ds.dfci.harvard.edu
SUMMARY:COVID-19 Data Science Zoomposium
DESCRIPTION:Caroline Buckee\, Department of Biostatistics\nHarvard TH Chan School of Public Health\nHow do we predict the pandemic? \nMichael Mina\, Department of Epidemiology\nHarvard TH Chan School of Public Health\nThe importance and challenges of testing for COVID-19 \nNatalie Dean\, Department of Biostatistics\, University of Florida\nHow we evaluate the efficacy of potential therapies and vaccines  \nAlexis Madrigal\, The Atlantic\nJournalism in the time of COVID-19 \nModerated by Rafael Irizarry \nSponsored by the Department of Data Sciences\, Dana-Farber Cancer Institute\nand the Brown Institute for Media Innovation\, Columbia University
URL:https://ds.dfci.harvard.edu/event/covid-19-data-science-zoomposium/
LOCATION:MA
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/png:https://ds.dfci.harvard.edu/wp-content/uploads/2020/06/logo.png
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END:VCALENDAR