It describes how to install and run Banjo, the parameters that the program requires, the names and formats of the data files that it uses, and how to put all the different pieces together to use Banjo flexibly with your own data.
The Banjo Developer Guide explains how to understand and modify Banjo source code.
It is intended to be read after the Banjo User Guide and describes the design and architecture of the Banjo application.
It also explains how to work with Banjo in the open source Eclipse development environment.
The Eclipse IDE is available as a free download from
Also, the Banjo code itself is documented internally.
The internal documentation can be viewed online in standard Java Doc format.
Our group has written a number of papers on Bayesian network inference, application of these methods to problems in systems biology, evaluation of these methods in a simulation framework, and extension and improvement of these methods for problems with small amounts of data.
Motivation: We have used state-space models (SSMs) to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T cell activation.
SSMs are a class of dynamic Bayesian networks in which the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics.
These hidden variables can capture effects that cannot be directly measured in a gene expression profiling experiment, for example: genes that have not been included in the microarray, levels of regulatory proteins, the effects of m RNA and protein degradation, etc.
Results: We have approached the problem of inferring the model structure of these state-space models using both classical and Bayesian methods.
In our previous work, a bootstrap procedure was used to derive classical confidence intervals for parameters representing ‘gene–gene’ interactions over time.
In this article, variational approximations are used to perform the analogous model selection task in the Bayesian context.