scholarly journals Bayesian Modeling in Genetics and Genomicsvvv

2017 ◽  
Author(s):  
Hafedh Ben Zaabza ◽  
Abderrahmen Ben Gara ◽  
Boulbaba Rekik
Keyword(s):  
Bayesian Modeling

Evaluation of the impact of public investment programs on social and economic performance of territories

2019 ◽  
pp. 109-123
Author(s):  
I. E. Limonov ◽  
M. V. Nesena
Keyword(s):  
Bayesian Modeling ◽  
Positive Impact ◽  
Public Investment ◽  
Difference In Differences ◽  
The Public ◽  
Export Activity ◽  
Regional Investment ◽  
The Difference ◽  
The Impact ◽  
Development Institution

The purpose of this study is to evaluate the impact of public investment programs on the socio-economic development of territories. As a case, the federal target programs for the development of regions and investment programs of the financial development institution — Vnesheconombank, designed to solve the problems of regional development are considered. The impact of the public interventions were evaluated by the “difference in differences” method using Bayesian modeling. The results of the evaluation suggest the positive impact of federal target programs on the total factor productivity of regions and on innovation; and that regional investment programs of Vnesheconombank are improving the export activity. All of the investments considered are likely to have contributed to the reduction of unemployment, but their implementation has been accompanied by an increase in social inequality.

scholarly journals Bayesian modeling of inconsistent plastic response due to material variability

2019 ◽  
Vol 353 ◽  
pp. 183-200 ◽  
Author(s):  
F. Rizzi ◽  
M. Khalil ◽  
R.E. Jones ◽  
J.A. Templeton ◽  
J.T. Ostien ◽  
...  
Keyword(s):  
Bayesian Modeling ◽  
Plastic Response

scholarly journals 1326. Vancomycin Area Under the Concentration-Time Curve (AUC) Estimation Using a Bayesian Approach Versus First-Order Pharmacokinetic Equations: A Pilot Study

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S673-S673
Author(s):  
Jeffrey Pearson ◽  
Yazed S Alsowaida ◽  
B S Pharm ◽  
David W Kubiak ◽  
Mary P Kovacevic ◽  
...  
Keyword(s):  
Pilot Study ◽  
Median Time ◽  
Bayesian Modeling ◽  
Surgical Prophylaxis ◽  
Time Curve ◽  
Team Members ◽  
First Order ◽  
Concentration Time ◽  
Study Team ◽  
Auc Estimation

Abstract Background Current guidelines endorse area under the concentration-time curve (AUC)-based monitoring over trough-only monitoring for systemic vancomycin. Vancomycin AUC can be estimated using either Bayesian modeling software or first-order pharmacokinetic (PK) calculations. The objective of this pilot study was to evaluate and compare the efficiency and feasibility of these two approaches for calculating the estimated vancomycin AUC. Methods A single-center crossover study was conducted in four medical/surgical units at Brigham and Women’s Hospital over a 3-month time period. All adult patients who received vancomycin were included. Patients were excluded if they were receiving vancomycin for surgical prophylaxis, were on hemodialysis, if vancomycin was being dosed by level, or if vancomycin levels were never drawn. The primary endpoint was the amount of time study team members spent calculating the estimated AUC and determining regimen adjustments with Bayesian modeling compared to first-order PK calculations. Secondary endpoints included the number of vancomycin levels drawn and the percent of those drawn that were usable for AUC calculations. Results One hundred twenty-four patients received vancomycin during the study, of whom 47 met inclusion criteria. The most likely reasons for exclusion were receiving vancomycin for surgical prophylaxis (n=40) or never having vancomycin levels drawn (n=32). The median time taken to assess levels in the Bayesian arm was 9.3 minutes [interquartile range (IQR) 7.8-12.4] versus 6.8 minutes (IQR 4.8-8.0) in the 2-level PK arm (p=0.004). However, if Bayesian software is integrated into the electronic health record (EHR), the median time to assess levels was 3.8 minutes (IQR 2.3-6.8, p=0.019). In the Bayesian arm, 30 of 34 vancomycin levels (88.2%) were usable for AUC calculations, compared to 28 of 58 (48.3%) in the 2-level PK arm. Conclusion With EHR integration, the use of Bayesian software to calculate the AUC was more efficient than first-order PK calculations. Additionally, vancomycin levels were more likely to be usable in the Bayesian arm, thereby avoiding delays in estimating the vancomycin AUC. Disclosures All Authors: No reported disclosures

scholarly journals scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Chantriolnt-Andreas Kapourani ◽  
Ricard Argelaguet ◽  
Guido Sanguinetti ◽  
Catalina A. Vallejos
Keyword(s):  
Single Cell ◽  
Bayesian Modeling ◽  
Bayesian Model ◽  
Regulation Of Gene Expression ◽  
Differential Methylation ◽  
Hierarchical Bayesian Model ◽  
Genomic Features ◽  
Distinct Cell ◽  
Biological Heterogeneity

AbstractHigh-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. scMET is available at https://github.com/andreaskapou/scMET.

scholarly journals Introducing the StataStan Interface for Fast, Complex Bayesian Modeling Using Stan

2017 ◽  
Vol 17 (2) ◽  
pp. 330-342 ◽  
Author(s):  
Robert L. Grant ◽  
Bob Carpenter ◽  
Daniel C. Furr ◽  
Andrew Gelman
Keyword(s):  
Bayesian Inference ◽  
Item Response Theory ◽  
Open Source ◽  
Item Response ◽  
Bayesian Modeling ◽  
Inference Engine ◽  
Software Components ◽  
Statistical Software ◽  
Simulation Based ◽  
Item Response Theory Models

In this article, we present StataStan, an interface that allows simulation-based Bayesian inference in Stata via calls to Stan, the flexible, open-source Bayesian inference engine. Stan is written in C++, and Stata users can use the commands stan and windowsmonitor to run Stan programs from within Stata. We provide a brief overview of Bayesian algorithms, details of the commands available from Statistical Software Components, considerations for users who are new to Stan, and a simple example. Stan uses a different algorithm than bayesmh, BUGS, JAGS, SAS, and MLwiN. This algorithm provides considerable improvements in efficiency and speed. In a companion article, we give an extended comparison of StataStan and bayesmh in the context of item response theory models.

scholarly journals Assessing the Effectiveness of Local Management of Coral Reefs Using Expert Opinion and Spatial Bayesian Modeling

PLoS ONE ◽  
2015 ◽  
Vol 10 (8) ◽  
pp. e0135465 ◽  
Author(s):  
Stephen S. Ban ◽  
Robert L. Pressey ◽  
Nicholas A. J. Graham
Keyword(s):  
Coral Reefs ◽  
Expert Opinion ◽  
Bayesian Modeling ◽  
Local Management

A rigorous approach for testing the constructionist hypotheses of brain function

2012 ◽  
Vol 35 (3) ◽  
pp. 148-149 ◽  
Author(s):  
Gopikrishna Deshpande ◽  
K. Sathian ◽  
Xiaoping Hu ◽  
Joseph A. Buckhalt
Keyword(s):  
Granger Causality ◽  
Strong Evidence ◽  
Bayesian Modeling ◽  
Brain Function ◽  
Brain Regions ◽  
Causal Modeling ◽  
Dynamic Causal Modeling ◽  
Rigorous Approach ◽  
Target Article ◽  
Co Activation

AbstractAlthough the target article provides strong evidence against the locationist view, evidence for the constructionist view is inconclusive, because co-activation of brain regions does not necessarily imply connectivity between them. We propose a rigorous approach wherein connectivity between co-activated regions is first modeled using exploratory Granger causality, and then confirmed using dynamic causal modeling or Bayesian modeling.

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