shinyBN is an R/Shiny
application for interactive construction, inference and visualization of Bayesian Network, which provide friendly GUI for users lacking of programming skills. It's mainly based on five R packages: bnlearn
for structure learning, parameter training, gRain
for network inference, and visNetwork
for network visualization, pROC
and rmda
for receiver operating characteristic (ROC) curve and decision curves analysis (DCA) , respectively, which was further wrapped by Shiny
, a framework to build interactive web application straight by R.
Run APP in R:
Install dependencies: ```{r,eval = FALSE} install.packages("devtools") library(devtools)
install.packages(c("shiny","shinydashboard","shinydashboardPlus","sqldf","writexl","readxl","reshape2","DT","bnlearn","ggsci","shinyjqui","ggplot2","visNetwork","pROC","rmda","knitr"))
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install(c("gRain","igraph","AnnotationDbi","EBImage"))
source("http://bioconductor.org/biocLite.R") biocLite(c("gRain","igraph","AnnotationDbi","EBImage"))
install_github(c("ramnathv/rblocks","woobe/rPlotter"))
Old sources
from CRAN, of which released before 2019 are proper.
Install *shinyBN* from Github:
```{r,eval = FALSE}
devtools::install_github('JiajinChen/shinyBN')
Launch the APP in R:
{r,eval = FALSE}
shinyBN::run_shinyBN()
Lauch the APP through browser:
Please visit: https://jiajin.shinyapps.io/shinyBN/ or http://bigdata.njmu.edu.cn/shinyBN/
Here, we provide Four type of data input:
Once your BN is inputed, the plot would present automatically with default parameters. If you are not satisfied with your graphic appearance, you can render your plot with corresponding settings. Additionally, network layout and legend can be set flexibly. Finally, shinyBN provides high-quality images download in HTML output and Network information in Excel.Because the network plot is based on canvas, it's difficult to get SVG. However, we provide a convenient way to get high-resolution images:
One of the major functions of Bayesian network is inference. You can query the probability of interested nodes given the values of a set of instantiated nodes. shinyBN allowed users to set multiple instantiated nodes and both marginal probability and joint probability are supported, the inference results will be displayed in bar plot or probabilistic table. Users can set different color representing different threshold to distinguish different levels of outcome probability. In addition, you can download the result through a PDF output interface for High-quality images.
shinyBN also allowed user to upload a validation set for batch inference. If your validation set contains outcome label, you can get the receiver operating characteristic (ROC) curve plot and decision curves analysis (DCA) plot. The same, both the plot in high-resolution images and batch prediction result in tables are supported.
If you have any problem or other inquiries you can also email us at ywei@njmu.edu.cn .
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