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Related Experiment Video

Updated: May 22, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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A hybrid Factored Frontier algorithm for Dynamic Bayesian Networks with a biopathways application.

Sucheendra K Palaniappan¹, S Akshay, Bing Liu

¹School of Computing, National University of Singapore, Singapore. suchee@comp.nus.edu.sg

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|April 25, 2012

Summary

A new Hybrid Factored Frontier (HFF) algorithm improves approximate inference for Dynamic Bayesian Networks (DBNs). HFF reduces errors in biochemical network modeling compared to existing Factored Frontier (FF) and Boyen-Koller (BK) methods.

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

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Published on: December 10, 2012

Area of Science:

Computational Biology
Systems Biology
Machine Learning

Background:

Dynamic Bayesian Networks (DBNs) are used for modeling biochemical networks.
Exact probability inference in large DBNs is computationally infeasible.
Existing approximate algorithms like Factored Frontier (FF) and Boyen-Koller (BK) can produce significant errors.

Purpose of the Study:

To introduce a novel approximate inference algorithm, the Hybrid Factored Frontier (HFF) algorithm.
To address the limitations of existing approximate inference methods for DBNs.
To improve the accuracy of probabilistic inference in large-scale biochemical models.

Main Methods:

The Hybrid Factored Frontier (HFF) algorithm is proposed.
HFF maintains probabilities of local states and explicitly tracks probabilities of selected global states (spikes).
The algorithm's performance is validated on large DBN models of biopathways with over 3,000 nodes.

Main Results:

HFF reduces inference errors compared to FF and BK algorithms.
Increasing the number of spikes in HFF leads to decreased errors.
The computational cost of HFF scales quadratically with the number of spikes, offering a tunable trade-off between accuracy and efficiency.

Conclusions:

HFF is a powerful and useful approximate inference algorithm for Dynamic Bayesian Networks.
The algorithm demonstrates superior performance in analyzing complex biopathway models.
HFF provides a more accurate and efficient approach to probabilistic inference in large DBNs.