A5063151241- Gene expression and cancer classification
- Statistical Methods and Inference
- Bioinformatics and Genomic Networks
- Nutritional Studies and Diet
- Bayesian Methods and Mixture Models
- Systemic Lupus Erythematosus Research
- Antifungal resistance and susceptibility
- Sensory Analysis and Statistical Methods
- Genetic and phenotypic traits in livestock
- Monoclonal and Polyclonal Antibodies Research
- Rheumatoid Arthritis Research and Therapies
- Genomics and Chromatin Dynamics
- Diverse academic and cultural studies
- Gut microbiota and health
- Diabetes and associated disorders
- SARS-CoV-2 and COVID-19 Research
- Migration, Ethnicity, and Economy
- Long-Term Effects of COVID-19
- Celiac Disease Research and Management
- COVID-19 epidemiological studies
- COVID-19 Clinical Research Studies
- Nutrition, Health and Food Behavior
- Statistical Methods and Bayesian Inference
- Advanced Statistical Methods and Models
- Fungal Infections and Studies
John Brown University
2021-2024
Brown University
2020-2024
Princeton University
2018-2021
University of Milan
2018
Bambino Gesù Children's Hospital
2005
Most patients with Post COVID Syndrome (PCS) present a plethora of symptoms without clear evidence organ dysfunction. A subset them fulfills diagnostic criteria myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Symptom severity ME/CFS correlates natural regulatory autoantibody (AAB) levels targeting several G-protein coupled receptors (GPCR). In this exploratory study, we analyzed serum AAB against vaso- and immunoregulatory receptors, mostly GPCRs, in 80 PCS following...
Autoantibodies have been associated with autoimmune diseases. However, studies identified autoantibodies in healthy donors (HD) who do not develop disorders. Here we provide evidence of a network immunoglobulin G (IgG) targeting protein-coupled receptors (GPCR) HD compared to patients systemic sclerosis, Alzheimer's disease, and ovarian cancer. Sex, age pathological conditions affect autoantibody correlation hierarchical clustering signatures, yet many the correlations are shared across all...
<h3>Importance</h3> The benefit of vaccination for preventing reinfection among individuals who have been previously infected with SARS-CoV-2 is largely unknown. <h3>Objective</h3> To obtain population-based estimates the probability and effectiveness associated after recovery from COVID-19. <h3>Design, Setting, Participants</h3> This cohort study used Rhode Island statewide surveillance data March 1, 2020, to December 9, 2021, on COVID-19 vaccinations, laboratory-confirmed cases,...
Abstract The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection is associated with increased levels of autoantibodies targeting immunological proteins such as cytokines and chemokines. Reports further indicate that COVID‐19 patients may develop a broad spectrum autoimmune diseases due to reasons not fully understood. Even so, the landscape induced by SARS‐CoV‐2 remains uncharted territory. To gain more insight, we carried out comprehensive assessment known be linked...
Abstract We introduce a novel class of factor analysis methodologies for the joint multiple studies. The goal is to separately identify and estimate (1) common factors shared across studies, (2) study-specific factors. develop an Expectation Conditional-Maximization algorithm parameter estimates we provide procedure choosing numbers specific present simulations evaluating performance method illustrate it by applying gene expression data in ovarian cancer. In both, clarify benefits compared...
Abstract The water crisis in Jackson, Mississippi, has recently made national and international headlines as a major environmental catastrophe, impacting the public health wellbeing of residents. Here we focus on Jackson’s most prevalent vulnerable population, its children, by assessing how boil alerts (BWAs) disrupt student learning. Using data BWAs collected from City Water/Sewer Business Administration Office between 2015 2021, daily school attendance Public School District...
Mutational signatures are patterns of somatic mutations in tumor genomes that provide insights into underlying mutagenic processes and cancer origin. Developing reliable methods for their estimation is growing importance biology. Somatic mutation data often collected different types, highlighting the need multi-study approaches enable joint analysis a principled integrative manner. Despite significant advancements, statistical models tailored analyzing multiple types remain underexplored. In...
Factors models are commonly used to analyze high-dimensional data in both single-study and multi-study settings. Bayesian inference for such relies on Markov Chain Monte Carlo (MCMC) methods, which scale poorly as the number of studies, observations, or measured variables increase. To address this issue, we propose new variational algorithms approximate posterior distribution latent factor using multiplicative gamma process shrinkage prior. The proposed provide fast at a fraction time memory...
A few papers have considered reproducibility of a posteriori dietary patterns across populations, as well pattern associations with head and neck cancer risk when multiple populations are available.We used individual-level pooled data from seven case-control studies (3844 cases; 6824 controls) participating in the International Head Neck Cancer Epidemiology consortium. We simultaneously derived shared study-specific novel approach called multi-study factor analysis applied to 23 nutrients....
Fungal infections represent a major global health problem affecting over billion people that kills more than 1.5 million annually. In this study, we employed an integrative approach to reveal the landscape of human immune responses Candida spp. through meta-analysis microarray, bulk, and single-cell RNA sequencing (scRNA-seq) data for blood transcriptome. We identified across these different studies consistent interconnected network interplay signaling molecules involved in both Toll-like...
This paper analyzes breast cancer gene expression across seven studies to identify genuine and thus replicable patterns shared among these studies. Our premise is that biological signal more likely be reproducibly present in multiple than spurious signal. analysis uses a new modeling strategy for the joint of high-throughput which simultaneously identifies as well study-specific To this end, we generalize multi-study factor model handle high-dimensional data sparse Bayesian infinite context....
Gaussian graphical models (GGMs) provide a framework for modeling conditional dependencies in multivariate data. In this tutorial, we an overview of GGM theory and demonstration various tools R. The mathematical foundations GGMs are introduced with the goal enabling researcher to draw practical conclusions by interpreting model results. Background literature is presented, emphasizing methods recently developed high-dimensional applications such as genomics, proteomics, or metabolomics....
Mutations in the BRCA1 and BRCA2 genes are known to be highly associated with breast cancer. Identifying both shared unique transcript expression patterns blood samples from these groups can shed insight into if how disease mechanisms differ among individuals by mutation status, but this is challenging high-dimensional setting. A recent method, Bayesian multistudy factor analysis (BMSFA), identifies latent factors common all studies (or equivalently, groups) specific individual studies....
Few observational studies investigated the relationship between single food groups and disease activity in rheumatoid arthritis (RA). Within a recent Italian cross-sectional study (365 patients, median age: 58.46 years, 78.63% females), we focused on two groups, olive oil nuts, representing vegetable sources of fatty acids. Disease was measured with Activity Score 28 joints based C-reactive protein (DAS28-CRP) Simplified Index (SDAI). Robust linear logistic regression models included...
Traditionally, research in nutritional epidemiology has focused on specific foods/food groups or single nutrients their relation with disease outcomes, including cancer. Dietary pattern analysis have been introduced to examine potential cumulative and interactive effects of individual dietary components the overall diet, which foods are consumed combination. patterns can be identified by using evidence-based investigator-defined approaches data-driven approaches, rely either response...
ABSTRACT The SARS-CoV-2 infection is associated with increased levels of autoantibodies targeting immunological proteins such as cytokines and chemokines. Reports further indicate that COVID-19 patients may develop a wide spectrum autoimmune diseases due to reasons not fully understood. Even so, the landscape induced by remains uncharted territory. To gain more insight, we carried out comprehensive assessment known be linked diverse observed in patients, cohort 248 individuals, which171 were...
Abstract Dietary patterns (DPs) synthesize multiple related dietary components in one or more combined variables. A drawback of DPs is their limited reproducibility across subpopulations, especially adopting a posteriori DPs, derived using standard multivariate methods [e.g., factor analysis (FA)]. Standard approaches assessing FA-based mostly rely on correlation coefficients/agreement measures between pairs factors and do not consider any statistical model. Multi-study builds upon FA model...
Understanding the trend of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) is becoming crucial. Previous studies focused on predicting COVID-19 trends, but few papers have considered models for disease estimation and progression based large real-world data. We used de-identified data from 60,938 employees a major financial institution in Italy with daily status information between 31 March 2020 August 2021. consider six statuses: (i) concluded case, (ii) confirmed (iii) close...
Bayesian factor models are widely used for dimensionality reduction and pattern discovery in high-dimensional datasets across diverse fields. These typically focus on imposing priors loading to induce sparsity improve interpretability. However, score, which plays a critical role individual-level associations with factors, has received less attention is assumed have standard multivariate normal distribution. This oversimplification fails capture the heterogeneity observed real-world...
Diet is a risk factor for many diseases. In nutritional epidemiology, studying reproducible dietary patterns critical to reveal important associations with health. However, it challenging: diverse cultural and ethnic backgrounds may critically impact eating patterns, showing heterogeneity, leading incorrect obscuring the components shared across different groups or populations. Moreover, covariate effects generated from observed variables, such as demographics other confounders, can further...