Recent Submissions

  • Bayesian graphical models for modern biological applications 

    Ni, Yang; Baladandayuthapani, Veerabhadran; Vannucci, Marina; Stingo, Francesco C. (2022)
    Graphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are ...
  • Fast, Optimal, and Targeted Predictions Using Parameterized Decision Analysis 

    Kowal, Daniel R. (2022)
    Prediction is critical for decision-making under uncertainty and lends validity to statistical inference. With targeted prediction, the goal is to optimize predictions for specific decision tasks of interest, which we represent via functionals. Although classical decision analysis extracts predictions from a Bayesian model, these predictions are often ...
  • Genomic Analysis of SARS-CoV-2 Alpha, Beta and Delta Variants of Concern Uncovers Signatures of Neutral and Non-Neutral Evolution 

    Kurpas, Monika Klara; Jaksik, Roman; Kuś, Pawel; Kimmel, Marek (2022)
    Due to the emergence of new variants of the SARS-CoV-2 coronavirus, the question of how the viral genomes evolved, leading to the formation of highly infectious strains, becomes particularly important. Three major emergent strains, Alpha, Beta and Delta, characterized by a significant number of missense mutations, provide a natural test field. We ...
  • Bayesian data synthesis and the utility-risk trade-off for mixed epidemiological data 

    Feldman, Joseph; Kowal, Daniel R. (2022)
    Much of the microdata used for epidemiological studies contain sensitive measurements on real individuals. As a result, such microdata cannot be published out of privacy concerns, and without public access to these data, any statistical analyses originally published on them are nearly impossible to reproduce. To promote the dissemination of key ...
  • Sequencing individual genomes with recurrent genomic disorder deletions: an approach to characterize genes for autosomal recessive rare disease traits 

    Yuan, Bo; Schulze, Katharina V.; Assia Batzir, Nurit; Sinson, Jefferson; Dai, Hongzheng; (2022)
    In medical genetics, discovery and characterization of disease trait contributory genes and alleles depends on genetic reasoning, study design, and patient ascertainment; we suggest a segmental haploid genetics approach to enhance gene discovery and molecular diagnostics.
  • Modes of Selection in Tumors as Reflected by Two Mathematical Models and Site Frequency Spectra 

    Kurpas, Monika K.; Kimmel, Marek (2022)
    The tug-of-war model was developed in a series of papers of McFarland and co-authors to account for existence of mutually counteracting rare advantageous driver mutations and more frequent slightly deleterious passenger mutations in cancer. In its original version, it was a state-dependent branching process. Because of its formulation, the tug-of-war ...
  • Racial residential segregation shapes the relationship between early childhood lead exposure and fourth-grade standardized test scores 

    Bravo, Mercedes A.; Zephyr, Dominique; Kowal, Daniel; Ensor, Katherine; Miranda, Marie Lynn (2022)
    Racial/ethnic disparities in academic performance may result from a confluence of adverse exposures that arise from structural racism and accrue to specific subpopulations. This study investigates childhood lead exposure, racial residential segregation, and early educational outcomes. Geocoded North Carolina birth data is linked to blood lead ...
  • The origin of bladder cancer from mucosal field effects 

    Bondaruk, Jolanta; Jaksik, Roman; Wang, Ziqiao; Cogdell, David; Lee, Sangkyou; (2022)
    Whole-organ mapping was used to study molecular changes in the evolution of bladder cancer from field effects. We identified more than 100 dysregulated pathways, involving immunity, differentiation, and transformation, as initiators of carcinogenesis. Dysregulation of interleukins signified the involvement of inflammation in the incipient phases of ...
  • Effective connectivity between resting-state networks in depression 

    DeMaster, Dana; Godlewska, Beata R.; Liang, Mingrui; Vannucci, Marina; Bockmann, Taya; (2022)
    Rationale Although depression has been widely researched, findings characterizing how brain regions influence each other remains scarce, yet this is critical for research on antidepressant treatments and individual responses to particular treatments. Objectives To identify pre-treatment resting state effective connectivity (rsEC) patterns in patients ...
  • Correlation of nuclear pIGF-1R/IGF-1R and YAP/TAZ in a tissue microarray with outcomes in osteosarcoma patients 

    Molina, Eric R.; Chim, Letitia K.; Lamhamedi-Cherradi, Salah-Eddine; Mohiuddin, Sana; McCall, David; (2022)
    Osteosarcoma (OS) is a genetically diverse bone cancer that lacks a consistent targetable mutation. Recent studies suggest the IGF/PI3K/mTOR pathway and YAP/TAZ paralogs regulate cell fate and proliferation in response to biomechanical cues within the tumor microenvironment. How this occurs and their implication upon osteosarcoma survival, remains ...
  • Bayesian graphical models for modern biological applications 

    Ni, Yang; Baladandayuthapani, Veerabhadran; Vannucci, Marina; Stingo, Francesco C. (2021)
    Graphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are ...
  • A simple tree planting framework to improve climate, air pollution, health, and urban heat in vulnerable locations using non-traditional partners 

    Hopkins, Loren P.; January-Bevers, Deborah J.; Caton, Erin K.; Campos, Laura A. (2022)
    Societal Impact Statement Planting trees is considered an effective method for climate change adaptation and mitigation. This framework provides a replicable blueprint to improve health, urban heat, flooding, and air pollution via a multisectoral, collaborative, environmental data-driven approach. Native tree species with targeted ecosystem services ...
  • Latent Network Estimation and Variable Selection for Compositional Data Via Variational EM 

    Osborne, Nathan; Peterson, Christine B.; Vannucci, Marina (2022)
    Network estimation and variable selection have been extensively studied in the statistical literature, but only recently have those two challenges been addressed simultaneously. In this article, we seek to develop a novel method to simultaneously estimate network interactions and associations to relevant covariates for count data, and specifically ...
  • A CRISPR toolbox for generating intersectional genetic mouse models for functional, molecular, and anatomical circuit mapping 

    Lusk, Savannah J.; McKinney, Andrew; Hunt, Patrick J.; Fahey, Paul G.; Patel, Jay; (2022)
    The functional understanding of genetic interaction networks and cellular mechanisms governing health and disease requires the dissection, and multifaceted study, of discrete cell subtypes in developing and adult animal models. Recombinase-driven expression of transgenic effector alleles represents a significant and powerful approach to delineate ...
  • The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider 

    Aarrestad, Thea; van Beekveld, Melissa; Bona, Marcella; Boveia, Antonio; Caron, Sascha; (2022)
    We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at detecting signals of new physics at the LHC using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent ...
  • Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods 

    Joubert, Bonnie R.; Kioumourtzoglou, Marianthi-Anna; Chamberlain, Toccara; Chen, Hua Yun; Gennings, Chris; (2022)
    Humans are exposed to a diverse mixture of chemical and non-chemical exposures across their lifetimes. Well-designed epidemiology studies as well as sophisticated exposure science and related technologies enable the investigation of the health impacts of mixtures. While existing statistical methods can address the most basic questions related to the ...
  • Heat shock factor 1 (HSF1) cooperates with estrogen receptor α (ERα) in the regulation of estrogen action in breast cancer cells 

    Vydra, Natalia; Janus, Patryk; Kus, Paweł; Stokowy, Tomasz; Mrowiec, Katarzyna; (2021)
    Heat shock factor 1 (HSF1), a key regulator of transcriptional responses to proteotoxic stress, was linked to estrogen (E2) signaling through estrogen receptor α (ERα). We found that an HSF1 deficiency may decrease ERα level, attenuate the mitogenic action of E2, counteract E2-stimulated cell scattering, and reduce adhesion to collagens and cell ...
  • A spatiotemporal case-crossover model of asthma exacerbation in the City of Houston 

    Schedler, Julia C.; Ensor, Katherine B. (2021)
    Case-crossover design is a popular construction for analyzing the impact of a transient effect, such as ambient pollution levels, on an acute outcome, such as an asthma exacerbation. Case-crossover design avoids the need to model individual, time-varying risk factors for cases by using cases as their own ‘controls’, chosen to be time periods for which ...
  • A Bayesian Nonparametric Spiked Process Prior for Dynamic Model Selection 

    Cassese, Alberto; Zhu, Weixuan; Guindani, Michele; Vannucci, Marina (2019)
    In many applications, investigators monitor processes that vary in space and time, with the goal of identifying temporally persistent and spatially localized departures from a baseline or “normal” behavior. In this manuscript, we consider the monitoring of pneumonia and influenza (P&I) mortality, to detect influenza outbreaks in the continental United ...
  • Filtering and Estimation for a Class of Stochastic Volatility Models with Intractable Likelihoods 

    Vankov, Emilian R.; Guindani, Michele; Ensor, Katherine B. (2019)
    We introduce a new approach to latent state filtering and parameter estimation for a class of stochastic volatility models (SVMs) for which the likelihood function is unknown. The α-stable stochastic volatility model provides a flexible framework for capturing asymmetry and heavy tails, which is useful when modeling financial returns. However, the ...

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