Category Cross-Omics>Pathway Analysis/Tools and Intelligent Software > Bayesian Network Systems/Tools

Abstract MARIMBA (Molecular Annotation Resource for Integrating Microarrays with Bayesian Analysis) is a web-based pipeline that can be used to model biological pathways using Bayesian networks.

Bayesian networks are graphical models that describe causal or apparently causal interactions between variables.

Bayesian networks are ideal for merging pathway models and gene expression data due to the network’s flexibility, robustness to error, and human interpretability.

Compared to other modeling methods, Bayesian networks can be learned automatically by searching through the space of network topologies and retaining the most significant top scoring networks.

Pathway models and microarray gene expression data are often the first resources used when searching for biological mechanisms. Many questions can be addressed by these resources, including:

1) Can gene expression data be used to successfully reconstruct a known pathway?

2) What other hidden molecular factors should be included to more fully understand the mechanism?

3) Can we model interactions among multiple pathways?

4) How can prior knowledge help refine pathway models?

MARIMBA targets to answer the above questions.

MARIMBA integrates gene expression data from publicly available databases [e.g., NCBI GEO (see G6G Abstract Number 20013) and the Alliance for Cellular Signaling (AfCS)] or user-defined data with existing pathway knowledge (e.g., KEEG Pathway Database).

MARIMBA provides a user-friendly graphical interface environment to simplify dataset selection, probe set/gene inclusion, observational file processing, settings selection, Bayesian network (BN) execution, and results visualization.

MARIMBA also offers automated data processing tools including fold change and clustering.

MARIMBA allows users to store and update their own data and modeling results.

New algorithms such as BN+1 have been developed to address fundamental pathway-related questions.

Software used by MARIMBA:

MARIMBA relies on the Banjo (Bayesian Network Inference with Java Objects) system (see G6G Abstract Number 20211) developed by Alex J. Hartemink at Duke University for Bayesian analysis. BANJO is implemented in MARIMBA in accord with the BANJO Non-Commercial Use License Agreement (2005).

"Banjo is licensed from Duke University. Copyright © 2005 by Alexander J. Hartemink. All rights reserved."

MARIMBA implements Pycluster, a freely available implementation of the C Cluster library for the Python programming language. "Copyright © 2002-2005 Michiel Jan Laurens de Hoon."

JPEG images (JPEG is a commonly used method of compression for photographic images) for network topology are generated using GraphViz software from AT&T. MARIMBA implements GraphViz in accord with the GraphViz Common Public License Version 1.0.

MARIMBA is also implemented using the following software tools:

System Requirements

Marimba is optimized using Firefox.


Manufacturer Web Site MARIMBA

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G6G Abstract Number 20210

G6G Manufacturer Number 101201