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X-WR-CALNAME:Farhad Shokoohi's Homepage
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200221T113000
DTEND;TZID=America/Los_Angeles:20200221T123000
DTSTAMP:20200116T193027Z
CREATED:20190917T162549Z
LAST-MODIFIED:20200116T193027Z
UID:622-1582284600-1582288200@farhad.faculty.unlv.edu
SUMMARY:Bayesian Nonparametric Differential Analysis for Dependent Multigroup Data with Application to DNA Methylation Analyses; By Subharup Guha\, Associate Professor\, Department of Biostatistics\, University of Florida
DESCRIPTION:Abstract: \n\n\n\nCancer ‘omics datasets involve widely varying sizes and scales\, measurement variables\, and correlation structures. An overarching scientific goal in cancer research is the development of general statistical techniques that can cleanly sift the signal from the noise in identifying genomic signatures of the disease across a set of experimental or biological conditions. We propose BayesDiff\, a nonparametric Bayesian approach based on a novel class of first order mixture models\, called the Sticky Poisson-Dirichlet process or multicuisine restaurant franchise. The BayesDiff methodology flexibly utilizes information from all the measurements and adaptively accommodates any serial dependence in the data\, accounting for the inter-probe distances\, to perform simultaneous inferences on the variables. The technique is applied to analyze the motivating DNA methylation gastrointestinal cancer dataset\, which displays both serial correlations and complex interaction patterns. In simulation studies\, we demonstrate the effectiveness of the BayesDiff procedure relative to existing techniques for differential DNA methylation. Returning to the motivating dataset\, we detect the genomic signature for four types of upper gastrointestinal cancer. The analysis results support and complement known features of DNA methylation as well as gene association with gastrointestinal cancer. \n(joint work with Chiyu Gu of Monsanto Company and Veerabhadran Baladandayuthapani of University of Michigan) \n\n\n\nWebsite: http://biostat.ufl.edu/about/people/faculty/guha-subharup/ \n 
URL:https://farhad.faculty.unlv.edu/seminar/2020-02-21/
LOCATION:CBC C217\, 4505 S Maryland Pkwy\, Las Vegas\, NV\, 89154\, United States
ORGANIZER;CN="Professor Kaushik Ghosh":MAILTO:kaushik.ghosh@unlv.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200207T113000
DTEND;TZID=America/Los_Angeles:20200207T123000
DTSTAMP:20200116T193043Z
CREATED:20190910T173017Z
LAST-MODIFIED:20200116T193043Z
UID:589-1581075000-1581078600@farhad.faculty.unlv.edu
SUMMARY:Learning Connectivity Networks from High-Dimensional Point Processes; By Ali Shojaie\,  Associate Professor of Biostatistics\, Department of Biostatistics University of Washington Seattle\, WA
DESCRIPTION:Abstract: \nHigh-dimensional point processes have become ubiquitous in many scientific fields. For instance\, neuroscientists use calcium florescent imaging to monitor the firing of thousands of neurons in live animals. In this talk\, I will discuss new methodological\, computational and theoretical developments for learning neuronal connectivity networks from high-dimensional point processes. Time permitting\, I will also discuss a new approach for handling non-stationarity in high-dimensional time series. \n  \nWebsite: http://faculty.washington.edu/ashojaie/ \n 
URL:https://farhad.faculty.unlv.edu/seminar/2020-02-07/
LOCATION:CBC C217\, 4505 S Maryland Pkwy\, Las Vegas\, NV\, 89154\, United States
ORGANIZER;CN="Farhad Shokoohi":MAILTO:farhad.shokoohi@unlv.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200124T113000
DTEND;TZID=America/Los_Angeles:20200124T123000
DTSTAMP:20200116T193105Z
CREATED:20190907T232321Z
LAST-MODIFIED:20200116T193105Z
UID:334-1579865400-1579869000@farhad.faculty.unlv.edu
SUMMARY:Statistical Approaches to Mitigating Inter-Scanner Differences in Magnetic Resonance Imaging Studies; By Russell Shinohara\, Associate Professor of Biostatistics\, University of Pennsylvania Perelman School of Medicine
DESCRIPTION:Abstract: \n\n\n\n\n\n\nWhile magnetic resonance imaging (MRI) studies are critical for the diagnosis\, monitoring\, and study for a wide variety of diseases\, their use in quantitative analysis can be complex. An increasingly recognized issue involves the differences between MRI scanners that are used in large multi-center studies. To address this\, the current state of the art is to “regress out” or “adjust for” scanner differences. Our group has found these methods to be insufficient\, and have advocated for the adaptation of methods pioneered in genomics to help mitigate inter- scanner differences which can vary across the brain and result in both mean and variance shifts. We further study the implications of differences in correlation structures across and between images\, and how this affects downstream inference. \n\n\n\n\n\n\nWebsite: https://www.dbei.med.upenn.edu/bio/russell-t-shinohara-phd
URL:https://farhad.faculty.unlv.edu/seminar/2020-01-24/
LOCATION:CBC C217\, 4505 S Maryland Pkwy\, Las Vegas\, NV\, 89154\, United States
ORGANIZER;CN="Farhad Shokoohi":MAILTO:farhad.shokoohi@unlv.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200123T120000
DTEND;TZID=America/Los_Angeles:20200123T130000
DTSTAMP:20200117T214521Z
CREATED:20190913T144256Z
LAST-MODIFIED:20200117T214521Z
UID:617-1579780800-1579784400@farhad.faculty.unlv.edu
SUMMARY:A statistical revisiting of multiple sclerosis lesions based on MRI; By Russell Shinohara\, Associate Professor of Biostatistics\, University of Pennsylvania Perelman School of Medicine
DESCRIPTION:Abstract: \nLesions in the white matter of the brain\, including those that arise in multiple sclerosis\, are abnormalities measurable on MRI. While much literature has focused on the identification of these lesions\, less work has focused on the nature of these lesions. As new imaging modalities arise that allow us to better interrogate these lesions\, new statistical modeling problems that include spatial constraints and overlapping domains of analysis are increasingly important. Leveraging multi-modal imaging approaches that focus on knowledge about etiology is critical for developing the next generation of robust and generalizable imaging biomarkers. \n\nWebsite: https://www.dbei.med.upenn.edu/bio/russell-t-shinohara-phd \n 
URL:https://farhad.faculty.unlv.edu/seminar/2020-01-23/
LOCATION:SEB 2251\, Science and Engineering Building Administration University of Nevada\, Las Vegas Box 454022 4505 S. Maryland Pkwy.\, Las Vegas\, NV\, 89154\, United States
ORGANIZER;CN="Farhad Shokoohi":MAILTO:farhad.shokoohi@unlv.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191115T113000
DTEND;TZID=America/Los_Angeles:20191115T123000
DTSTAMP:20190918T172032Z
CREATED:20190909T173432Z
LAST-MODIFIED:20190918T172032Z
UID:566-1573817400-1573821000@farhad.faculty.unlv.edu
SUMMARY:Gene Graph-based Imputation for scRNA-seq Data; By Zuoheng Wang\, Associate Professor of Biostatistics\, Yale School of Public Health
DESCRIPTION:Abstract: \nSingle-cell RNA sequencing (scRNA-seq) technology provides higher resolution of gene expression to study the cellular level expression heterogeneity in different tissues. However\, one major challenge in scRNA-seq data analysis is the low capture efficiency that results in a large proportion of zero in the data matrix. For genes with low or moderate expression\, this leads to unreliable reads that may obstruct downstream analysis. We propose G2S3\, a gene-graph based imputation method which borrows the information across neighboring genes on the graph to denoise the expression data and filling the dropout. G2S3 learns a sparse graph structure from each gene’s expression profile under the assumption that biological signal changes smoothly between genes closely residing on the graph. We then harness this gene network to impute the data matrix by construct a network-based diffusion process. We demonstrated through real data based on down-sampling experiments that G2S3 can accurately recover the true expression level\, improve clustering results of cell populations and differential expression analysis. G2S3 can also restore the gene-gene regulatory relationship which might be obscured by the dropouts. Lastly\, G2S3 is computationally efficient for large scRNA-seq datasets and is able to impute data with hundreds of thousands of cells which has become available with the advance of sequencing technology. \nWebsite: https://publichealth.yale.edu/profile/zuoheng_wang/
URL:https://farhad.faculty.unlv.edu/seminar/2019-11-15/
LOCATION:CBC C235\, 4505 S Maryland Pkwy\, Las Vegas\, NV\, 89154\, United States
ORGANIZER;CN="Professor Amei Amei":MAILTO:amei.amei@unlv.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191101T113000
DTEND;TZID=America/Los_Angeles:20191101T123000
DTSTAMP:20190917T162231Z
CREATED:20190909T174458Z
LAST-MODIFIED:20190917T162231Z
UID:574-1572607800-1572611400@farhad.faculty.unlv.edu
SUMMARY:Generalized Spacings Estimators; By S. Rao Jammalamadaka\, Professor\, University of California Santa Barbara
DESCRIPTION:Abstract: \n\n\n\nSpacings\, which are the gaps between successive observations\, have been utilized in statistical inference both for estimation and in testing of hypotheses. After a brief review of this area\, we introduce estimators based on higher-order or multi-step spacings\, called the “Generalized Spacings Estimators (GSEs)”. Such estimators are obtained by minimizing the so-called Csiszar divergence between the empirical and the true distributions. Maximum likelihood estimators (MLEs) can be viewed as a special case\, and GSEs are clearly needed when the MLEs do not exist. Current results generalize much of the earlier work on spacings-based estimation. These estimators are shown to be consistent as well as asymptotically normal under quite general conditions. When the step size and the number of spacings grow with the sample size\, an asymptotically efficient class of estimators\, called the “Minimum Power Divergence Estimators\,” are shown to exist. Simulation studies show that these asymptotically efficient estimators\, perform very well in finite samples relative to the MLEs\, and unlike the MLEs\, are quite robust even under heavy contamination. \n\n\n\nWebsite: https://www.pstat.ucsb.edu/people/s-rao-jammalamadaka
URL:https://farhad.faculty.unlv.edu/seminar/2019-11-01/
LOCATION:CBC C235\, 4505 S Maryland Pkwy\, Las Vegas\, NV\, 89154\, United States
ORGANIZER;CN="Professor Kaushik Ghosh":MAILTO:kaushik.ghosh@unlv.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191018T113000
DTEND;TZID=America/Los_Angeles:20191018T123000
DTSTAMP:20190918T171943Z
CREATED:20190909T174838Z
LAST-MODIFIED:20190918T171943Z
UID:578-1571398200-1571401800@farhad.faculty.unlv.edu
SUMMARY:Bayesian Disease Progression Modeling in Clinical Trials; Scott Berry\, PhD\, Berry Consultants
DESCRIPTION:Abstract: \nFrequently primary analyses in clinical trials of progressive diseases use change-from-baseline type analyses\, such as the MMRM.  These analyses of the absolute changes at different time points ignores that the disease is progressive\, and the mechanism of the intervention is to slow progression. \nIn this talk I’ll present several example in which a Bayesian primary analysis is created that analyzes whether an intervention slows the rate of progression of a disease\, rather than the absolute change.  The analysis can create a great deal of improved precision — as well as improved clinical interpretations.  Multiple examples will be discussed\, including dominantly inherited Alzheimer’s\, GNE Myopathy\, and Batten’s Disease.
URL:https://farhad.faculty.unlv.edu/seminar/2019-10-18/
LOCATION:CBC C235\, 4505 S Maryland Pkwy\, Las Vegas\, NV\, 89154\, United States
ORGANIZER;CN="Professor Petros Hadjicostas":MAILTO:petros.hadjicostas@unlv.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191004T113000
DTEND;TZID=America/Los_Angeles:20191004T123000
DTSTAMP:20190910T230514Z
CREATED:20190907T225530Z
LAST-MODIFIED:20190910T230514Z
UID:323-1570188600-1570192200@farhad.faculty.unlv.edu
SUMMARY:Recurrent Events Analysis With Data Collected at Informative Clinical Visits in Electronic Health Records; By Chiung-Yu Huang\, Professor\, Department of Epidemiology & Biostatistics\, University of California\, San Francisco
DESCRIPTION:Abstract:  \nAlthough increasingly used as a data resource for assembling cohorts\, electronic health records (EHRs) pose many analytic challenges because they are primarily collected for clinical encounters rather than for research purposes. In particular\, a patient’s health status influences when and what data are recorded\, generating sampling bias in the collected data. In this paper\, we consider recurrent event analysis using EHR data. Conventional regression methods for event risk analysis usually require the values of covariates to be observed throughout the follow-up period. In EHR databases\, time-dependent covariates are intermittently measured during clinical visits\, and the timing of these visits is informative in the sense that it depends on the disease course. Simple methods\, such as the last-observation-carried-forward approach\, can lead to biased estimation. On the other hand\, complex joint models require additional assumptions on the covariate process and cannot be easily extended to handle multiple longitudinal predictors. By incorporating sampling weights derived from estimating the observation time process\, we develop a novel estimation procedure based on inverse-rate-weighting and kernel-smoothing for the semiparametric proportional rate model of recurrent events. The proposed methods do not require model specifications for the covariate processes and can easily handle multiple time-dependent covariates. The estimators for the regression parameters are asymptotically unbiased and normally distributed with a root-n convergence rate. Simulation studies are conducted to evaluate the performance of the proposed estimator. Our methods are applied to a kidney transplant study for illustration. (Joint work with Yifei Sun\, Charles  McCulloch\, Kieren Marr\, and Chiung-Yu Huang) \nWebsite: https://profiles.ucsf.edu/chiung-yu.huang
URL:https://farhad.faculty.unlv.edu/seminar/2019-10-04/
LOCATION:CBC C235\, 4505 S Maryland Pkwy\, Las Vegas\, NV\, 89154\, United States
ORGANIZER;CN="Farhad Shokoohi":MAILTO:farhad.shokoohi@unlv.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191003T143000
DTEND;TZID=America/Los_Angeles:20191003T153000
DTSTAMP:20190926T163543Z
CREATED:20190910T225918Z
LAST-MODIFIED:20190926T163543Z
UID:606-1570113000-1570116600@farhad.faculty.unlv.edu
SUMMARY:Efficient Estimation of the Cox Model With Auxiliary Subgroup Survival Information; By Chiung-Yu Huang\, Professor\, Department of Epidemiology & Biostatistics\, University of California\, San Francisco
DESCRIPTION:Abstract: \nWith the rapidly increasing availability of data in the public domain\, combining information from different sources to infer about associations or differences of interest has become an emerging challenge to researchers. We present a novel approach to improve efficiency in estimating the survival time distribution by synthesizing information from the individual-level data with t-year survival probabilities from external sources such as disease registries. While disease registries provide accurate and reliable overall survival statistics for the disease population\, critical pieces of information that influence both choices of treatment and clinical outcomes usually are not available in the registry database. To combine with the published information\, we propose to summarize the external survival information via a system of nonlinear population moments and estimate the survival time model using empirical likelihood methods. The proposed approach is more flexible than the conventional meta-analysis in the sense that it can automatically combine survival information for different subgroups and the information may be derived from different studies. Moreover\, an extended estimator that allows for a different baseline risk in the aggregate data is also studied. Empirical likelihood ratio tests are proposed to examine whether the auxiliary survival information is consistent with the individual-level data. Simulation studies show that the proposed estimators yield a substantial gain in efficiency over the conventional partial likelihood approach. (Joint work with Jing Qin and Huei-Ting Tsai) \n\nWebsite: https://profiles.ucsf.edu/chiung-yu.huang
URL:https://farhad.faculty.unlv.edu/seminar/2019-10-03/
LOCATION:HRC\, The Harry Reid Center (HRC)\, Las Vegas\, NV\, 89154\, United States
ORGANIZER;CN="Farhad Shokoohi":MAILTO:farhad.shokoohi@unlv.edu
END:VEVENT
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