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

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Sparse time series chain graphical models for reconstructing genetic networks.

Fentaw Abegaz¹, Ernst Wit

¹Johann Bernoulli Institute of Mathematics and Computer Science, University of Groningen, Nijenborgh 9, The Netherlands. f.abegaz.yazew@rug.nl

Biostatistics (Oxford, England)

|March 7, 2013

Summary

This summary is machine-generated.

We developed a novel sparse high-dimensional time series graphical model to reconstruct genetic networks from gene expression data. This method reveals both immediate and evolving gene interactions for biological discovery.

Keywords:

Chain graphical mode Dynamic network Gene expression High-dimensional data L1 penalty Model selection Penalized likelihood SCAD penalty Vector autoregressive model

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Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

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Area of Science:

Computational Biology
Bioinformatics
Systems Biology

Background:

Gene expression data provides insights into complex biological processes.
Understanding genetic regulatory networks is crucial for deciphering cellular functions.
High-dimensional time series data presents unique challenges for network inference.

Purpose of the Study:

To propose a novel sparse high-dimensional time series chain graphical model.
To reconstruct genetic networks using gene expression data.
To explore contemporaneous and dynamic interactions within genetic networks.

Main Methods:

Utilizing a precision matrix and autoregressive coefficient matrix parametrization.
Treating time steps as blocks or chains.
Combining Gaussian graphical models and Bayesian dynamic networks.
Employing penalized likelihood inference with a smoothly clipped absolute deviation penalty.

Main Results:

The model efficiently explores patterns of contemporaneous and dynamic interactions.
Successfully applied to simulated data for validation.
Demonstrated efficacy on real-world gene expression datasets from Arabidopsis thaliana and mammary glands.

Conclusions:

The proposed time series chain graphical model is effective for genetic network reconstruction.
The method offers a robust approach for analyzing time course gene expression data.
Facilitates a deeper understanding of gene regulatory dynamics.