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Influence of network topology and data collection on network inference.

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Date
2003
Authors
Smith, V Anne
Jarvis, Erich D
Hartemink, Alexander J
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Abstract
We recently developed an approach for testing the accuracy of network inference algorithms by applying them to biologically realistic simulations with known network topology. Here, we seek to determine the degree to which the network topology and data sampling regime influence the ability of our Bayesian network inference algorithm, NETWORKINFERENCE, to recover gene regulatory networks. NETWORKINFERENCE performed well at recovering feedback loops and multiple targets of a regulator with small amounts of data, but required more data to recover multiple regulators of a gene. When collecting the same number of data samples at different intervals from the system, the best recovery was produced by sampling intervals long enough such that sampling covered propagation of regulation through the network but not so long such that intervals missed internal dynamics. These results further elucidate the possibilities and limitations of network inference based on biological data.
Type
Journal article
Subject
Algorithms
Animals
Bayes Theorem
Computational Biology
Computer Simulation
Gene Expression Profiling
Gene Expression Regulation
Humans
Models, Genetic
Songbirds
Vocalization, Animal
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https://hdl.handle.net/10161/11221
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Scholars@Duke

Hartemink

Alexander J. Hartemink

Professor in the Department of Computer Science
Computational biology, machine learning, Bayesian statistics, transcriptional regulation, genomics and epigenomics, graphical models, Bayesian networks, hidden Markov models, systems biology, computational neurobiology, classification, feature selection
Jarvis

Erich David Jarvis

Adjunct Professor in the Dept. of Neurobiology
Dr. Jarvis' laboratory studies the neurobiology of vocal communication. Emphasis is placed on the molecular pathways involved in the perception and production of learned vocalizations. They use an integrative approach that combines behavioral, anatomical, electrophysiological and molecular biological techniques. The main animal model used is songbirds, one of the few vertebrate groups that evolved the ability to learn vocalizations. The generality of the discoveries is tested in other vocal
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